University of Montana ScholarWorks at University of Montana
Graduate Student Theses, Dissertations, & Professional Papers Graduate School
2014
Interindividual Variability in the Cytochrome P450 3A4 Drug Metabolizing Enzyme: Effect of the CYP3A4*1G Genetic Variant
Kasse Jean Skagen The University of Montana
Follow this and additional works at: https://scholarworks.umt.edu/etd Let us know how access to this document benefits ou.y
Recommended Citation Skagen, Kasse Jean, "Interindividual Variability in the Cytochrome P450 3A4 Drug Metabolizing Enzyme: Effect of the CYP3A4*1G Genetic Variant" (2014). Graduate Student Theses, Dissertations, & Professional Papers. 4351. https://scholarworks.umt.edu/etd/4351
This Thesis is brought to you for free and open access by the Graduate School at ScholarWorks at University of Montana. It has been accepted for inclusion in Graduate Student Theses, Dissertations, & Professional Papers by an authorized administrator of ScholarWorks at University of Montana. For more information, please contact [email protected]. INTERINDIVIDUAL VARIABILITY IN THE CYTOCHROME P450 3A4
DRUG METABOLIZING ENZYME: EFFECT OF THE CYP3A4*1G
GENETIC VARIANT
By
Kasse Jean Skagen
Biochemistry, Western Washington University, Bellingham, WA, 2008
Thesis
presented in partial fulfillment of the requirements for the degree of
Master of Science in Pharmaceutical Sciences
The University of Montana Missoula, MT
August 2014
Approved by:
J.B. Alexander Ross, Dean of The Graduate School Graduate School
Erica Woodahl, PhD, Chair Department of Biomedical and Pharmaceutical Sciences
Howard Beall, PhD Department of Biomedical and Pharmaceutical Sciences
Keith Parker, PhD Department Biomedical and Pharmaceutical Sciences
J. Stephen Lodmell, PhD Division of Biological Sciences
Skagen, Kasse, Master of Science, Summer 2014 Pharmaceutical Sciences
Interindividual Variability in the Cytochrome P450 3A4 Drug Metabolizing Enzyme: Effect of the CYP3A4*1G Genetic Variant
Chairperson: Erica Woodahl, PhD
Researchers and clinicians are interested in how a patient’s individual genetic makeup could predict the appropriate medication and dose for that patient. One way to predict drug response, or efficacy, is by looking at enzymes within the liver that metabolize drugs. Many of these enzymes belong to a class called the Cytochrome P450s (CYPs). Specifically, two closely related enzymes, CYP3A4 and CYP3A5, are involved in metabolizing 50% of drugs currently on the market (eg: statins, antiepileptics, anticancer agents, and antidepressants). There can be differences in the genetic code of these enzymes that can causes changes in drug metabolism.
We completed a study with participants from the Confederated Salish and Kootenai Tribes (CSKT), located on the Flathead Reservation in northwest Montana. Select CYP enzymes were genotyped, including CYP3A4 and CYP3A5. Most SNPs identified in the CSKT participants were found at frequencies similar to those reported in European-descended populations. Interestingly, one specific SNP, called CYP3A4*1G, was discovered at a high allele frequency. The physiological significance of this SNP is unclear as there are limited and confounding data, however, most of the data published to date suggest that the SNP causes decreased metabolism of drugs. Clinically, this could result in a need for a decreased dose of medication. In addition, this CYP3A4 SNP was observed to be often inherited with another SNP in the related CYP3A5 gene, called CYP3A5*3, which encodes a nonfunctional enzyme. These SNPs found in the CSKT are of particular interest, because inheriting these two SNPs together could cause drastic changes in drug metabolism since the two enzymes metabolize many of the same drugs.
ii Table of Contents
List of Tables……………………………………………………...…………………….vi
List of Figures………………………………………………………………………….vii
1. Introduction…………………………………………………………………………..1
A. The Promise of Pharmacogenomics………………………………………..2
i. Pharmacogenetics: Improving Outcomes…………………………..4
ii. Genotype-Phenotype Associations………………………………….5
iii. Source of Genetic Variability in Drug Response and Toxicity……7
B. Cytochrome P450s…………………………………………………………..10
i. CYP Evolution………………………………………………………..11
ii. CYP Structure and Conserved Regions…………………………..12
iii. CYP Biochemistry and Catalytic Cycle……………………………13
iv. CYP Regulation………………………………………………………14
C. Academic-Community Research Partnership with the
Confederated Salish and Kootenai Tribes………………………………...15
i. CYP2C9 Resequencing……………………………………………..16
ii. CYP2D6 Resequencing……………………………………………..17
iii. CYP3A4 and CYP3A5 Resequencing……………………………..18
iv. Implications…………………………………………………………...22
v. CYP3A4*1G Data……………………………………………………23
D. CYP3A Subfamily……..……………………………………………………..26
i. CYP3A4……………………………………………………………….26
ii. CYP3A4 Variability…………………………………………………..27
iii iii. CYP3A4 Phenotypes………………………………………………..28
iv. CYP3A5……………………………………………………………….28
v. CYP3A5 Phenotypes………………………………………………..28
vi. CYP3A4 and CYP3A5 Linkage Disequilibrium…………………...28
E. Specific Aims…………………………………………………………………30
2. Lymphocytes as Surrogates of CYP3A Drug Metabolism……………………39
A. Introduction…………………………………………………………………...40
B. Materials and Methods………………………………………………………42
i. Cells…………………………………………………………………...42
ii. RNA Isolation and cDNA Synthesis………………………..………42
iii. Quantitative Real-time PCR…………………………………...……43
iv. CYP3A4 Protein Quantitation in Lymphocytes…….……………..44
v. CYP3A4 Protein Quantitation in Human Liver Microsomes….....44
vi. CYP3A4 Activity in Lymphocytes…………………………………..45
vii. Data Analysis…………………………………………………………45
C. Results………………………………………………………………………..45
i. CYP3A4 mRNA Expression in Lymphocytes……………………..45
ii. CYP3A4 Protein Expression in Lymphocytes…………………….47
iii. CYP3A4 Activity in Lymphocytes…………………………………..47
D. Discussion…………………………………………………………………….47
iv 3. Effect of the Genetic Variant CYP3A4*1G in Human Liver Microsomes.…..59
A. Introduction……………………………………………………………...……60
B. Materials and Methods………………………………………………………62
i. Human Liver Microsomes……………………..……………………62
ii. CYP3A4 Activity in Human Liver Microsomes……………………63
iii. Protein Quantitation in Human Liver Microsomes………………..64
iv. Data Analysis…………………………………………………………64
C. Results………………………………………………………………………..64
i. Optimization of Microsomal Incubation Conditions………………64
ii. Subject Demographics………………………………………………64
iii. Analysis of CYP3A4 Activity……………………………………...... 66
D. Discussion…………………………………………………….………………67
4. Summary and Future Directions………………………………………………..75
Bibliography…………………………………………………………………………….80
v List of Tables
1. Introduction
Table 1.1. List of CYP2C9, CYP2D6, and CYP3A4/5 Common
Substrates Inhibitors, and Inducers……….…………………….30
Table 1.2. Major CYP2C9 Alleles……………………………………………31
Table 1.3. Major CYP2C9 Allele Frequencies……………….……………..31
Table 1.4. Major CYP2D6 Alleles……………………………………………32
Table 1.5. Major CYP2D6 Allele Frequencies……………………………...33
Table 1.6. Major CYP3A4 Alleles…………………………………………….34
Table 1.7. Major CYP3A4 Allele Frequencies..…………………………….34
Table 1.8. Major CYP3A5 Alleles…………………….………………………35
Table 1.9. Major CYP3A5 Allele Frequencies……………………………...35
2. Lymphocytes as Surrogates of CYP3A Drug Metabolism
Table 2.1. TaqMan® Probe Information………………...…………………..52
Table 2.2. Cts by Genotype…………………………………………………..53
Table 2.3. Cts of Template Dilution……………………….…………………54
3. Effect of the Genetic Variant CYP3A4*1G in Human Liver Microsomes
Table 3.1. Human Liver Microsome Demographics………………………..69
vi List of Figures
1. Introduction
Figure 1.1.Cytochrome P450 Cycle……….…………………………………36
Figure 1.2. SNP Gene Map………………………….….……………………37
2. Lymphocytes as Surrogates of CYP3A Drug Metabolism
Figure 2.1. qPCR Traces of Lymphocytes………………….……..………..55
Figure 2.2. CYP3A4 and CYP3A5 Normalized Expression Levels ……...56
Figure 2.3. qPCR Traces of Template Dilution………………...…………..57
Figure 2.4. Immunoblot and Quantitation of CYP3A4 Protein….…………58
3. Effect of the Genetic Variant CYP3A4*1G in Human Liver Microsomes
Figure 3.1. Optimization of CYP3A4 Activity in Pooled Human Liver
Microsomes………………………………………………………70
Figure 3.2. CYP3A4 Interindividual Variability Measured in Human
Liver Microsomes…………………………………………..……71
Figure 3.3. CYP3A4 Activity in Human Liver Microsomes………….……..72
Figure 3.4. Relationship Between Human Liver Microsome Total
Protein Content and CYP3A4 Rate………………………..…..73
vii
Chapter 1: Introduction
1 1.A. The Promise of Pharmacogenomics
Pharmacogenomics offers a new way of practicing medicine by individualizing medications and dosages based on an individual’s genetic make-up [1, 2]. The goal is to optimize efficacy while minimizing adverse events [3]. The completion of the Human Genome Project in 2000 allowed scientists to more easily link specific genetic changes to differences in drug response and toxicity [1, 2].
The Federal Drug Administration (FDA) states that its “mission is to protect and promote the health of all Americans through assuring the safety, efficacy, and security of drugs…” [4]. The FDA believes personalized medicine has potential to increase efficacy and decrease risk of adverse drug reactions [4]. They have released guidelines to better integrate genetic information with medications [4-6]
[3]. These are guidelines for new drug applications as well as when, how, and what pharmacogenomic data to submit [3]. They have also required that pharmacogenomic data be included in the product insert of 140 different medications, many with more than one predictive biomarker; the importance of these biomarkers can vary from drug choice, to dosage, to black box warnings [4].
Medications with pharmacogenomic data in the product insert are widespread: trastuzumab (oncology), tamoxifen (oncology), phenytoin (neurology), warfarin
(hematology/cardiology), clopidogrel (cardiology), abacavir (infectious diseases), atorvastatin (endocrinology), codeine (anesthesiology), and fluoxetine
(psychiatry). This information is available to the public on their website under the
Table of Pharmacogenomic Biomarkers in Drug Labeling [7]. Also in 2007, the
2 FDA approved the first genotyping test, a DNA microarray, Amplichip CYP450.
This is used by physicians to assist in medication and dosage choices [8].
Another organization that releases pharmacogenetics-guided dosing recommendations is the Clinical Pharmacogenetics Implementation Consortium
(CPIC). The CPIC was formed in 2009 and focuses on providing guidelines designed to help providers understand how genetic test results should be used to optimize drug therapy in clinical practice [9]. To date, they have identified 174 gene-drug pairs of interest, which include 63 unique genes and 131 unique drugs.
CPIC has published guidelines on 33 of these pairs. These 174 gene-drug pairs are broken down into levels (A, B, C, or D) that indicate level of evidence and strength of recommendation. Level A indicates there is evidence to change prescribing regimen of drug, while Level D indicates there is weak or conflicting evidence and no changes in the prescribing regimen are recommended at this time. Codeine, phenytoin, simvastatin, and warfarin are considered Level A.
Fluoxetine, tamoxifen, and omeprazole are considered Level B. Level C drugs include propranolol and diazepam, and Level D drugs include aspirin, atorvastatin, caffeine, and metformin [10].
One CPIC Level A drug is tacrolimus, a medication given to patients who undergo solid organ or hematopoietic cell transplantation to prevent rejection.
Tacrolimus has a very narrow therapeutic window and plasma levels are strictly monitored by therapeutic drug monitoring; too little drug leads to organ or graft
3 rejection while too much drug leads to nephrotoxicity [11-13]. In spite of individual monitoring, patients still experience lack of efficacy or adverse events. The clearance of tacrolimus is mediated by drug-metabolizing enzymes, cytochrome
P450 3A4 (CYP3A4) and 3A5 (CYP3A5), which determine drug levels in the body. In vitro and in vivo data show that individuals with genotypes encoding for deleterious CYP3A5 enzyme function have lower clearances and higher trough concentrations than those expressing wildtype enzyme [14-18]. Using CYP3A4 and CYP3A5 genotypes to more accurately determine tacrolimus dosage regimens can improve efficacy through less dosage modifications and quicker time to target tacrolimus plasma concentrations [19].
1.A.i. Pharmacogenetics: Improving Outcomes
Most medications used today are efficacious in only 25% (oncology medications) to 80% (analgesic medications) of patients [4, 20]. Although efficacy can be affected by several factors such as patient compliance, diet, and drug interactions, genetic variations can play a large role [3]. Identifying patients at risk for adverse events can help to minimize injuries as well as reduce medical costs.
Adverse drug reactions (ADRs) refer to significant side effect(s) of medications, some of which can be life threatening [21-23]. The Institute of Medicine reports that there are at least 1.5 million preventable ADRs in the United States (US) per year and they are considered the leading cause of preventable death [24, 25]. As a result of ADRs, there are more than 100,000 deaths per year in the US costing
$100 billion per year [26]. Pharmacogenomics may be able to play a role in
4 reducing these adverse events by predicting those at higher risk due to a change in drug metabolism.
Interindividual variability in drug response and toxicity is multifactorial and include both extrinsic and intrinsic factors. Extrinsic factors include the environment (i.e. smoking, diet, and alcohol consumption) and drug interactions (i.e. concomitant use of other prescription medications, over-the-counter medications, and herbal supplements). Intrinsic factors include demographics (age, gender, and ethnicity) and disease (particularly liver and kidney dysfunction). Finally, genetic variation between individuals can also be a key player in differences in response and toxicity [27-30]. It has been proposed that genetic factors can account for as much as 20-95% of interindividual variability in drug disposition [31]. This wide estimation accounts for different effects of genetic factors in different gene-drug pairs. The amount of interindividual variability can be drug specific; it will affect metabolism rates differently depending on the drug given. Also, drug elimination pathways can be very complex, causing genetic factors to have different effects.
1.A.ii. Genotype-Phenotype Associations
Genotype-phenotype association studies are important in order to make a prediction about how a patient’s genetic variation, or their genotype, can affect the response or toxicity to a given medication, known as their phenotype. Genetic variation can affect the outcome of about a quarter of all medications [29]. It is
5 important to identify individuals carrying these variations so that alternative medications or doses may be chosen.
Most genotype-phenotype correlations can be measured through in vivo or in vitro probe drug assays [32]. Probe drugs are metabolized by a single drug- metabolizing enzyme and are administered to identify the function of that enzyme.
Phenotypes can be measured through administration of a subtherapeutic dose of probe drug [33]. When using probe drugs to measure a phenotype, plasma and urine concentrations of the parent drug and metabolite are measured in order to estimate the pharmacokinetics of the parent and metabolite(s) [33, 34]. An advantage of this method is that phenotype is directly measured under current conditions (diet, age, disease state, etc). However, there are several disadvantages. This method can have complicated protocols and there is a risk of determining the wrong phenotype to do concurrent medications or disease state. There is also a risk of an ADR in patients of extreme phenotypes [34].
However, in clinical practice, phenotype is typically measured by a clinical outcome (i.e. INR for warfarin or reducing LDL levels for statins) [35].
There are four phenotypes: Poor Metabolizers (PM), Intermediate Metabolizers
(IM), Extensive Metabolizers (EM), and Ultra Metabolizers (UM). PMs do not express active enzyme. This can cause increase risk of toxicity if the medication is toxic but decreased efficacy if metabolite is active. IMs have reduced enzyme activity. These patients continue to have lower metabolism than the standard
6 population. EMs express fully active enzyme and standard doses are given to these patients. UMs have multiple copies of functional enzyme. These patients may have increased risk for toxicity if the metabolite is toxic or decreased efficacy if the parent medication is active [8, 33, 34, 36].
Genotyping allows practitioners to optimize the drug choice and dose for each individual and avoid most ADRs. These can lead to decreased medical costs [34].
Genotyping can have the largest clinical impact on patients taking narrow therapeutic medications, patients with unexplained side effects, as well as, older patients. Older patients tend to take more medications and are more likely to exhibit serious side effects; they also can display large changes in metabolism due to decreased liver and kidney function [37].
1.A.iii. Sources of Genetic Variability in Drug Response and Toxicity
Pharmacogenetic variability results from genetic variation in both pharmacokinetic and pharmacodynamic properties of a drug. Pharmacokinetics is the study of the effect of the body on drugs, while pharmacodynamics is the study of the effects of drugs on the body. Alterations in pharmacokinetic pathways alter the drug exposure in an individual and make up the vast majority of pharmacogenetic variability.
The pharmacokinetic disposition of a xenobiotic can be broken down by the processes of absorption, distribution, metabolism, and elimination (ADME) [38].
7 Absorption describes the processes that control the rate and extent of absorption of a compound from the site of administration to the systemic circulation. These processes include passive diffusion (small, lipophilic xenobiotics), active or facilitated transport (large, polar, or charged xenobiotics), and first-pass extraction in the gastrointestinal tract and liver for drugs that are orally administered [39]. After xenobiotics have entered systemic circulation, they are distributed from the vasculature to various tissues of the body, including those where they exert pharmacologic, and perhaps toxic, effect. The extent of this distribution depends upon passive and active diffusion rates across membranes and protein binding in both blood and tissues [38]. Metabolism mainly occurs in the liver, however, other tissues are known to have some metabolism such as kidney, lungs, and intestines [40]. Substrates undergo a wide range of metabolic reactions. Most undergo Phase I metabolism where the substrate undergoes oxidation, reduction, or hydrolysis reactions. Next, drugs often undergo sequential Phase II metabolism, which are conjugative reactions [38]. Hepatic metabolism is a primary component in the clearance of many compounds.
Metabolism also plays a large role in first-pass extraction in the intestine and liver and, consequently, has a large effect on bioavailability of compounds that are extensively metabolized [38, 41]. Elimination of xenobiotics includes both the processes of metabolism and excretion. The primary routes of excretion are biliary excretion, via transporters, in the liver [42] and urinary excretion in the kidneys, by glomerular filtration and passive and active secretion via transporters
[38, 43].
8
Phase I enzymes are made up of mostly heme thiolate proteins called cytochrome P450s (CYPs) that facilitate hydroxylation, reduction, and oxidation reactions to convert lipophilic compounds to more hydrophilic compounds that are more easily excreted [34, 37, 44, 45]. These enzymes metabolize a wide variety of medications, steroids, fatty acids, and procarcinogens [46]. Examples of CYPs are CYP1A2, CY2C9, CYP2C19, CYP2D6, and CYP3A4 [38]. Phase II enzymes are conjugative by taking advantage of the hydrophilic groups added by
Phase I enzymes. These conjugative reactions are mainly glucuronidation, sulfation, acetylation, and methylation. Examples of these enzymes are UDP- glucuronosyltransferases (UGTs), sulfotransferases (SULTs), and glutathione S- transferases (GSTs) [34, 37, 38]. Drug transporters are important in transporting hydrophilic drugs, metabolites, and conjugated metabolites across cell membranes and facilitating their elimination in the bile and urine. Drug transporters are also important in mediating delivery of drugs or their metabolites to their therapeutic target. Important examples of drug transporters are P- glycoprotein (P-gp), multidrug resistance-associated proteins (MRPs), organic anion transporters (OATs), organic cation transporters (OCTs), and organic anion transporting polypeptides (OATPs) [34, 47, 48].
Genetic variation in drug-metabolizing enzymes and drug transporters are of key importance in pharmacogenomics. A variety of genetic modifications can cause alterations to these proteins, including gene deletions and duplications, known as copy number variation (CNV), insertions and deletions, as well as point mutations,
9 known as single nucleotide polymorphisms (SNPs). A polymorphism is defined as a genetic mutation that is present at a frequency of greater than 1% [34].
Genetic variation can occur in coding (exons) or noncoding (introns, 5’UTR and
3’UTR) regions [3].
1.B. Cytochrome P450s
The cytochrome P450 family is the most important class of enzymes in overall drug metabolism, accounting for 78% of metabolism of drugs cleared through the liver [49]. The Human Genome Project has identified 57 active enzymes and 58 pseudogenes [37, 50-52]. CYPs are a superfamily of hemeproteins, which are found in the smooth endoplasmic reticulum (ER) membrane. These enzymes have two main roles: metabolize exogenous drugs and xenobiotics as well as metabolize hormones, vitamins, and fatty acids [53]. They are expressed in several extrahepatic tissues including the intestine, lung, kidney, and brain but are preferentially expressed in the centrilobular area of the liver [34, 37, 54].
“Cyto” refers to microsomal vesicles while “chrome” refers to color. The “P” alludes to the pigmentation, red, conferred by the heme, and 450 refers to the maximum absorbance of 450 nm when the enzyme is bound to carbon monoxide
[8, 36, 37, 55, 56].
There are 16 human families of CYPs [33, 34, 36, 55]. A family is defined as enzymes that have ≥40% amino acid identity and are designated by a number.
Families are divided into subfamilies that have ≥ 55% amino acid identity and are designated by a number. Finally, numbers are given to designate different genes
10 [36, 37, 55, 57]. There are 3 major groups of CYPs: 1) CYP1-3 families have lower affinity for substrates and are less conserved but are responsible for 70-
80% of all Phase I metabolism 2) CYP4 family metabolizes fatty acids and some xenobiotics and 3) CYP5-51 families have high affinity for endogenous substrates and are relatively well conserved [34, 37].
1.B.i. CYP Evolution
The first cytochrome is believed to have arisen less than 3.5 billion years ago and to have functioned anaerobically [58, 59]. Since then, CYPs have undergone multiple rounds of expansion facilitated by gene duplication. The first round occurred around 1.5 billion years ago. This expansion gave rise to CYPs that metabolized fatty acids and cholesterol [60]. The next expansion occurred around 900 million years ago. This resulted in CYPs that metabolize steroids.
One of these CYPs later gave rise to current day CYP families 1 and 2. Finally,
400 million years ago, CYPs underwent another round of expansion. This resulted in several CYP families that metabolize xenobiotics. This last expansion is thought to be driven by aquatic organisms vast change in diet upon introduction onto land as well as terrestrial organisms introduction to combustion products [61, 62].
Despite multiple distinct rounds of expansion, most CYP families are continuously changing. Gene duplication allows for one copy to diverge while the other copy maintains its original function. This often creates a pseudogene,
11 however it sometimes increases the overall fitness of the organism. For example,
CYP often arose by metabolizing toxic substances from organisms’ diets [62].
Because CYPs are beneficial in order to process a wide variety of changing toxins, they have become a rapidly evolving gene. Change in a gene is measured by length of time for a unit of evolutionary period. CYPs unit of evolutionary period is about 2 – 4 million years. However, histones unit of evolutionary period is about 400 million years while immunoglobulins are about
700,000 years [61, 62].
1.B.ii. CYP Structure and Conserved Regions
Most CYPs have around 480 to 560 amino acids. They can be categorized into three groups based on location: 1) ER membrane (microsomal-type) 2) mitochondrial membrane (mitochondrial-type) or 3) cytosol (rare in eukaryotes)
[57]. Microsomal CYPs are differentiated by their signal-anchor sequence, located at the N-terminal, which targets the enzyme to the endoplasmic reticulum membrane. This signal domain is made up of 20-25 hydrophobic residues and is distinguished by charged residues on either side, basic residues toward the C- terminal and negative residues near the N-terminal. These charged residues ensure that the CYP is properly inserted into the membrane, luminal side of the
ER with enzyme exposed to cytoplasm. The hydrophobic region serves as the stop-transfer sequence [57, 63-65]. However, mitochondrial CYPs have a mitochondria-targeted sequence instead. This sequence acts to stall the folding
12 of the catalytic site until enzyme is transported into the mitochondria where it is then cleaved [57].
Although the CYP superfamily share only 20% sequence identity, they do share overall folding and topology [66]. They have an alpha helix rich carboxy-terminal and a beta-sheet rich amino-terminal [46]. Parallel helices (D, L, and I) as well as antiparallel helix (E) make up the common structure [67]. The heme binds between helix I and L to the Cys-heme-ligand loop containing the sequence,
FxxGx(H/R)xCxG; the cysteine is essential as if forms the fifth ligand to the heme
[68, 69]. Helix I is located near the center of the enzyme and within the heme pocket. It contains the conserved sequence (G/A)Gx(D/E)T. The threonine residue is part of the oxygen-binding pocket and is involved in catalysis [70-72].
Another conserved sequence, EXXR, is located in helix K. This sequence is key for enzyme function [69].
There are six substrate recognition sites located within helices F, G, and I [73].
These sites affect substrate specificity and are flexible to accommodate better substrate binding [74]. Any genetic changes that alter amino acid residues within regions can cause changes in drug affinities and metabolism [73].
1.B.iii. CYP Biochemistry and Catalytic Cycle
CYPs oxidize various toxins, medications, and endogenous substrates.
Mechanistically, these enzymes split molecular oxygen to incorporate one as a
13 functional group on the substrate while the other is released as a water molecule.
This reaction requires an electron donor, the reducing agent NADPH. The
+ + general formula is NADPH + O2 + SH + H -> NAD(P) + SOH + H2O [46]. Figure
1.1 illustrates the general CYP catalytic cycle. CYPs remain in an unreactive state until binding of the substrate. The first step is a transfer of an electron from cytochrome P450 reductase to reduce the iron from 3+ to 2+ charge. Next, the
CYP complex binds molecular oxygen and triggers another electron transfer from either cytochrome P450 reductase or cytochrome b5. Two protons are accepted and the iron returns to a 3+ state. Finally, an oxygen atom is transferred to the substrate. The oxidized substrate is then released [75, 76]. There are three abortive steps (Figure 1.1), called uncoupling, within this cycle that return the enzyme to its resting state. Each produce either a superoxide anion, hydrogen peroxide, or water and occur at different intermediate states [77].
1.B.iv. CYP Regulation
There are four different types of CYP regulation: xenobiotic-inducible [78], sex-
specific, tissue-specific [79], and developmental [80] regulation. CYP induction
is an important mechanism in protecting an organism from changing toxins. For
example, phenobarbital is a known broad CYP inducer [78, 81]. There are three
receptors present in the cytosol that detect toxins. The pregnane X-receptor
(PXR) regulates CYP2C9 and CYP3A4 [82-85], the aryl hydrocarbon receptor
(AhR) regulates CYP1A1 and CYP1A2 [86], while constitutive androgen
receptor (CAR) regulates CYP2C9 and CYP3A4 [82-85]. There have been
14 polymorphisms reported in all three of these receptors that effect CYP
expression [87]. CYPs are also regulated in a sex-specific manner through the
endocrine system and gonadal hormones. The endocrine system also plays a
role in tissue-specific regulation [88].
1.C. Academic-Community Research Partnership with the Confederated
Salish and Kootenai Tribes
The Confederated Salish and Kootenai Tribes (CSKT) are located in northwest
Montana on the Flathead Indian Reservation. Three tribes reside on this reservation as part of CSKT: the Bitterroot Salish, Upper Pend d’Oreille, and
Kootenai. Our laboratory is involved in a research partnership with CSKT to explore the use of pharmacogenomics within the tribe. With personalized medicine on the rise, some populations with health disparities are not always included in research, and therefore, do not benefit from gene-guided therapies.
There is little known about pharmacogenomic variation within understudied populations, like American Indian populations. Because allele frequencies are diverse among world populations, allele frequencies within American Indians cannot be assumed to be similar to other studied populations [89, 90]. It is important to investigate frequencies of genetic variants in all populations in order to utilize pharmacogenomic testing.
Members of our laboratory have met with Tribal Council, Tribal Health, and a community advisory board to assure there is proper approval before any
15 research began. The community stated their main interest was in anticancer pharmacogenomics, mainly tamoxifen. CYP2C9, CYP2D6, CYP3A4, and
CYP3A5 are responsible for 75% of all phase I drug metabolism, including several other anticancer agents. Therefore, our laboratory analyzed blood samples from tribal volunteers to explore the genetic variation with CYP2C9,
CYP2D6, CYP3A4, and CYP3A5. Research participants, 18 years and older, were recruited at powwows, health fairs and career fairs and were asked tribal affiliation and blood quanta. DNA was extracted from whole blood and CYP2D6
(entire gene) was resequenced in 187 participants, while CYP2C9, CYP3A4, and
CYP3A5 (exons and flanking intron regions) was resequenced in 94 random participants [90].
1.C.i. CYP2C9 Resequencing
CYPC9 makes up 20% of hepatic CYP content and metabolizes about 15% of medications currently on the market, several with narrow therapeutic windows.
Table 1.1 lists common substrates, inducers, and inhibitors for CYP2C9.
Examples of substrates are warfarin, ibuprofen, and phenytoin [3, 36, 37, 46].
There are two important variants of CYP2C9, CYP2C9*2 and CYP2C9*3 (Table
1.2) that can cause large interindividual variability as well as cause adverse events [91, 92]. Together, these alleles are in seen in about 18% of European descendants, but much less so in other populations (Table 1.3). CYP2C9*2 and
CYP2C9*3 encode for proteins with reduced intrinsic clearance. This effect is substrate specific but can caused reduced activity up to 90% [92, 93].
16
In resequencing CYP2C9 in the CSKT population, our laboratory found 41 SNPs,
11 novel (most with very low frequencies). Also CYP2C9*2 and CYP2C9*3 were found to at a frequency lower than those found in European descendants. These
SNPs cause decrease function but, due to their low frequencies, may play a minor role in CYP2C9 interindividual variability in the CSKT. Also, low level of linkage was seen between CYP2C9 SNPs. Table 1.3 lists the allele frequencies of interesting SNPs identified in the CSKT population.
1.C.ii. CYP2D6 Resequencing
CYP2D6 is another important drug-metabolizing enzyme in the CYP2 family.
There are several polymorphisms of CYP2D6 that can cause a large clinical impact (Table 1.4). Although it only makes up two percent of total hepatic CYP content, it takes part in the metabolism of 15% of drugs on the market. CYP2D6 metabolizes several different substrates: propranolol, paroxetine, trazodone, codeine, and fentanyl (Table 1.1) [46, 49]. CYP2D6 is the only non-inducible
CYP, so genetic variation accounts for much of the interindividual variability [49,
94]. There are more than 80 known variants, which could drastically change metabolism [95]. PMs are more common in Caucasians with 5-10% expressing a null allele. However, only 0-1% of Africans and Asians are classified as PMs. The most common allele responsible for the PM phenotype is CYP2D6*4. IMs are more common in Asians with 50% expressing the CYP2D6*10 allele. Only, 10-
15% of Caucasians are classified as IMs, expressing the CYP2D6*41 allele,
17 encoding for a SNP that causes a fraction to missplice, and 30% of Africans express the CYP2D6*17 allele. UMs are more common in African populations.
The frequency of gene duplications is present in up to 50% in some populations and can cause up to 30-fold higher amounts of metabolite [94, 96]. It is thought that gene duplications evolved as a result of dietary pressure [94].
Upon resequencing CYP2D6 in the CSKT population, our lab found 76 SNPs with 9 identified as novel. Individuals with multiple copies of CYP2D6 were found to be low in this population (1.34% of alleles). The major SNPs were found to be at similar frequencies found in Caucasians (Table 1.5); 1.1% are UM, 87.2% are
EM, 3.2% are IM, and 5.9% are PM. However, there was a high level of linkage seen between CYP2D6 SNPs, including several novel haplotypes identified. The functional consequences of these haplotypes are unclear.
1.C.iii. CYP3A4 and CYP3A5 Resequencing
CYP3A4 is the highest expressed CYP in the liver and intestine, making up to
60% of total hepatic CYP expression [97]. Also, its presence in the small intestine is a large factor of first-pass effect [98]. CYP3A4 metabolizes a large range of substrates, totaling more than 120 different medications (Table 1.1), such as midazolam, saquinavir, erythromycin, diazepam, verapamil, tacrolimus, and simvastatin. CYP3A4 also metabolizes procarcinogens and endogenous substrates like testosterone and progesterone [99].
18 Most variants within CYP3A4 occur at a frequency of less than 5% and rarely occur has homozygotes, [30, 32, 100-105] although, not all have been well characterized. Figure 2.2 shows a map of the exons and introns of CYP3A4 with relative locations of major SNPs.
CYP3A4*1B has been identified in the 5’UTR, however, there is confounding data as to its clinical effect. Some investigators report this SNP causes decreased nuclear protein binding [106] and its presence has been linked to different diseases such as prostate cancer [107, 108]. However, in vivo and in vitro data using probe drugs are not so clear. Using a luciferase expression assay, investigators report an increased rate of expression for the CYP3A4*1B allele [109, 110]. Although not significant, it has also been reported that human livers expressing this SNP have an increased rate of nifedipine metabolism.
However, other in vitro and in vivo studies show no such association between genotype and phenotype [101, 111, 112].
Coding SNPs seem to be more localized within exons 5-7 and 11-12 and frequencies are reported to be low (<5%). Most SNPs result in minimal, if any, change in drug metabolism. Those that do change enzyme activity, appear to do so in a substrate dependent fashion. CYP3A4*2 causes decreased clearance and a 6-fold increase in the Km in nifedipine metabolism, however, no change in the metabolism rate of testosterone [102, 113]. CYP3A4*3 encodes for a SNP within the heme-binding pocket, although, no change in clearance is observed
19 [103, 104, 114]. CYP3A4*8 and CYP3A4*13 are reported to have low protein content in a cells and are expected to cause decreased levels in vivo [103, 104].
CYP3A4*12 results in an increased clearance of testosterone 15β and 2β- hydroxylation [103]. CYP3A4*17 is reported to cause a reduction in testosterone metabolism while CYP3A4*18 causes an increase in testosterone metabolism
[104].
Intronic SNPs are more rare and are mostly present at a frequency of less than
1%, however, a few are reported at much higher frequencies. G20338A and
T15871C are present at approximately 50% in African American and 6.5% in
Caucasians. Interestingly, these two alleles are commonly inherited together in
African Americans, although no clinical significance has been reported [100].
CYP3A4*1G is another SNP, located in intron 10, seen at higher frequencies in various populations [115]. There is mixed data, although, most suggest decreased clearance [116-120]. CYP3A4*22, located in intron 6, is found in
Caucasian populations [121, 122]. Again, this SNP has mixed data, however, most suggest it leads to decreased clearance [122-126].
Resequencing CYP3A4 in CSKT populations resulted in identification of 15 SNPs, of which 4 are novel SNPs. Major SNPs identified are listed in Table 1.6. All
SNPs were seen with frequencies similar to Caucasians, except for CYP3A4*1G, seen with a frequency similar to Japanese and Chinese populations (26.8%)
(Table 1.7). This results in 7% of individuals with the homozygous,
20 CYP3A4*1G/*1G genotype, and 39% as heterozygous for *1G. This SNP has conflicting data as to its clinical relevance and will be discussed in section 1.C.v.
CYP3A5 is expressed in the liver, and is the only CYP3A isoform expressed outside the liver and intestine tissues, such as the kidney [127], prostate [128], and lung [129, 130]. CYP3A5 has a similar structure to CYP3A4 and metabolizes many of the same substrates, however, it usually does so with slower turnover rates [131, 132]. Because CYP3A4 and CYP3A5 have similar substrates, there is no known specific substrate of CYP3A5. This makes it difficult to measure
CYP3A5 specific activity [129].
Polymorphisms with the largest clinical impact can be divided into coding region variants and intronic SNPs that cause frameshifts or splicing defects [129].
Figure 2.2 shows a map of introns and exons and the location of the major SNPs.
SNPs have been reported within the 5’ untranslated region, CYP3A5*1B and
CYP3A5*1C, however, they are seen to have no clinical significance [100, 133].
CYP3A5*6, *7, *8, *9, and *10 are all SNPs within the coding region that result in change in enzyme function. CYP3A5*6 results in a truncated, nonfunctional protein [100, 129]. CYP3A5*7 actually encodes for a base insertion that causes a frameshift, and again, a nonfunctional protein [129, 133, 134]. CYP3A5*8 and
CYP3A5*9 encode for amino acid changes that result in roughly 50% enzyme activity. CYP3A5*10 results in an inactive enzyme through an amino acid change in the heme-binding region [129].
21
CYP3A5*3 and CYP3A5*5 are clinically important intronic SNPs [129].
CYP3A5*3 is the most common polymorphism seen in CYP3A5. This intronic
SNP causes a splice variant with integration of a portion of intron 3. This leads to a frameshift and premature termination. This deleterious SNP is more commonly found in Caucasians and Asians, however, Africans more often express the functional, wild-type, enzyme [100, 133]. CYP3A5*5 causes a change in a base in the splicing donor site and results in truncated protein [129, 134].
Resequencing of CYP3A5 in CSKT populations resulted in identification of 10
SNPs; 1 was novel and found at a low frequency. Other major identified SNPs are listed in Table 1.8. CYP3A5*3 was found at frequency of 92.47%, similar to
Caucasians (Table 1.9). This results in 86% of the CSKT population as homozygous for CYP3A5*3, 14% are heterozygous individuals, and zero were homozygous for CYP3A5*1 (wild-type).
Overall, CYP3A4 and CYP3A5 exhibit a high level of linkage. However, there was a break in the linkage between CYP3A4*1G and CYP3A5*1 (0.158 LD). The clinical relevance of this linkage will be explored in following section, Implications.
1.C.iv. Implications
Despite novel SNPs found in CYP2C9, CYP2D6, CYP3A4, and CYP3A5, they are not expected to play a large role in interindividual variability because of their
22 low frequencies. Most common allele frequencies were found to be similar to those found in Caucasians. Although, one SNP, CYP3A4*1G, was found at a much higher frequency. CYP3A4*1G frequency is much more similar to
Japanese and Chinese populations. However, unlike all other populations where
CYP3A4*1G is found in high linkage with CYP3A5*1, CSKT has a novel break in the linkage between CYP3A4*1G and CYP3A5*1. In combination, the relatively common CYP3A4*1G (26.81% allele frequency) and the high frequency
CYP3A5*3 (92.47% allele frequency) in the CSKT could have large clinical implications because CYP3A4*1G data suggest lower activity [116-120] and
CYP3A5*3 encodes for a nonfunctional protein [100, 133]. Individuals carrying
CYP3A4*1G and CYP3A5*3 could have severely diminished CYP3A activity.
1.C.v. CYP3A4*1G Data
CYP3A4*1G is an intronic SNP found within intron 10 [135]. Again, most data suggest diminished activity for CYP3A4*1G. There have been several studies investigating the effect of CYP3A4*1G on fentanyl consumption post gynecological surgery [116, 117, 119, 120]. Zhang et al. found a trend of decreased fentanyl consumption for CYP3A4*1G carriers [119], while Dong et al. found a statistical difference of decreased fentanyl consumption between only
CYP3A4*1G/*1G status with both the heterozygote and wild-type [117]. Zhang et al., interestingly, found a significant decrease in fentanyl consumption for patients carrying both CYP3A4*1G/*1G and CYP3A5*3/*3 [116]. This haplotype is seen in high frequency within the CSKT and could change their drug metabolism. More
23 recently, Yuan et al. analyzed the effect of CYP3A4*1G on fentanyl consumption as well as plasma concentrations [120]. They found that patients who expressed
CYP3A4*1G/*1G had statistically higher fentanyl plasma concentrations and required lower fentanyl doses than those who expressed heterozygote or wild- type genotypes. All patients in all studies were female; CYP3A4 genotype could have a higher impact on females, because they are reported to express more
CYP3A4 [136]. With higher metabolism rates due to more protein content, changes in activity can results in larger changes in metabolism rates.
A trend of decreasing function of CYP3A4*1G was found when investigating its effect on atorvastatin efficacy [118]. A gene-dose effect was found on the mean reduction of serum total cholesterol after atorvastatin treatment. However, this effect was not seen after simvastatin treatment, suggesting potential substrate- specific effects.
There are also a few studies that suggest CYP3A4*1G is a gain-of-function SNP.
Miura et al. found that tacrolimus pharmacokinetics were significantly altered in patients carrying CYP3A4*1G and were CYP3A5 expressers; these patients had lower exposures and initial concentrations [137]. Zuo et al. also found this same effect. Patients carrying CYP3A4*1G and CYP3A5*1 had the highest tacrolimus clearance than other haplotypes. Also, CYP3A4*1G carriers and CYP3A5 nonexpressers also had higher clearance than those expressing CYP3A4*1/*1 and CYP3A5*3/*3 [138]. He et al. investigated the effect of CYP3A4*1G on
24 coronary heart disease (CHD) risk. Using a multivariate regression, they found that those who express CYP3A4*1G/*1G are at increased risk of CHD. The authors suggest that CYP3A4*1G is a gain of function. They argue that because
CYP3A4 metabolizes estrogen, and estrogen is protective against CHD, that those who carry CYP3A4*1G must metabolize estrogen more quickly [139].
However, no probe drugs were used to actually assay CYP3A4*1G clearance.
Another study, looking at CYP3A4*1G effect on risperidone metabolism, found no significant difference between plasma risperidone concentrations [140].
However, those expressing CYP3A4*1G/*1G did have much lower plasma levels.
This study performed a Kruskal-Wallis analysis between all three genotypes groups. As in other studies, perhaps they would have found significance if they had grouped CYP3A4*1/*1 with CYP3A4*1/*1G and analyzed their plasma concentrations with CYP3A4*1G/*1G. Because risperidone is mainly metabolized by CYP2D6, the authors suggest that changes in CYP3A4 function would be of greater impact in those who are CYP2D6 poor metabolizers. CYP2D6 was not genotyped, which again, may have resulting in CYP3A4*1G significance when
CYP2D6 genotype was accounted for.
The combined data suggest a possible substrate-dependent effect of
CYP3A4*1G. However, most data do indicate CYP3A4*1G results in decreased clearance. More research needs to be done to identify the effect of CYP3A4*1G as well as when inherited with CYP3A5*3/*3.
25 1.D. CYP3A Subfamily
The CYP3A subfamily comprises four functional genes, CYPs 3A4, 3A5, 3A7, and 3A43, as well as two pseudogenes [129, 141]. All six genes are located inline with one another on chromosome 7 [129]. This subfamily shares many substrates but differ in tissue expression [34, 46, 129]. CYP3A4 metabolizes around 50% of drugs currently on the market and could, therefore, be considered one of the most important drug metabolizing enzymes (Table 1.1) [3, 34, 36, 46,
129, 142-144]. Common SNPs in CYP3A5 encode for deleterious protein and, therefore, is variably expressed. CYP3A7 is expressed in fetal livers up until about 6 months of age. However, 10% of adult livers continue to express
CYP3A7 and can contribute up to almost a quarter of total CYP3A content, which can contribute to clearance [145-147]. CYP3A7 expression into adulthood exists more in Japanese populations, with 3A7 accounting for up to 40% of 3A content
[147]. CYP3A43 has been found in several tissues, however, at very low quantities. Also, it exhibits reduced activity towards testosterone, so it is not expected to play much of a role in xenobiotic metabolism [148].
1.D.i. CYP3A4
The CYP3A4 gene is 27kb and includes 13 exons and 12 introns. The gene encodes for a 57 kDa protein made up of 502 amino acids [57, 89, 149, 150].
Substrates of CYP3A4 are large and lipophilic [49]. CYP3A4 can metabolize a wide range of structural substrates due to its large and flexible binding pocket
26 [151, 152]. It has been known to bind multiple substrates at once which can cause increased or decreased product formation [153].
1.D.ii. CYP3A4 Variability
It has been estimated that 90% of CYP3A4 interindividual variation is due to genetic factors [154]. Variability in CYP3A4 expression can cause a dramatic clinical effect due to the large number of substrates [30]. There is large interindividual variability seen in CYP3A4 expression, up to a 40-fold change
[112, 155, 156]. However, most populations tend to lie within a 4- to 6-fold variation [106, 156-158]. Genetic variants do not account for all the variability, however, there are numerous factors that need to be considered [34, 129]. There have been over 20 variants reported that can explain for some variation. Also,
CYP3A4 can be induced, through increased transcription, by certain xenobiotics, such as rifampicin, barbiturates, carbamazepine, glucocorticoids, and St. John’s
Wort [34, 36, 46, 159]. CYP3A4 can also be inhibited by various xenobiotics.
Ketoconazole, saquinavir, fluoxetine, and grapefruit juice have been reported to as inhibitors. Potent inhibitors can cause plasma levels of the drug to increases
20-fold [34, 36, 160]. CYP3A4 is the only P450 that is expressed at different levels between sexes, with women expresses up to 2-fold more protein than men
[136]. Finally, some interindividual variation may exist due to hormonal regulation of CYPs. Hormones can be endogenously circulating or present in diet [28]. All these factors can make determining genotype-phenotype correlations difficult.
27 1.D.iii. CYP3A4 Phenotypes
Phenotypes seen from variation in CYP3A4 are unimodal, unlike what is seen in
CYP2D6. In vivo treatment with midazolam resulted in outliers exhibiting higher clearances [158]. However, when treated with nifedipine, outliers were present with lower clearances [161]. This unimodal distribution suggests that no single factor can be used to predict CYP3A4 phenotype [30].
1.D.iv. CYP3A5
CYP3A5 has 13 exons and is made up of 502 amino acids [32, 162]. Unlike
CYP3A4, CYP3A5 is not markedly induced [163]. However, CYP3A5 is polymorphic with the most common SNP encoding for deleterious protein [164].
1.D.v. CYP3A5 Phenotypes
In individuals who express wild-type CYP3A5 enzyme, CYP3A5 can make up
50% of CYP3A content [100]. This can result in large interindividual variability in
CYP3A metabolism; those carrying the CYP3A5*3/*3 genotype metabolize midazolam at less than half the rate of those carrying at least one CYP3A5*1 functional allele [100, 156]. Due to the dramatic decrease in activity, CYP3A5 genotype has been correlated with statin treatment side effects[165].
1.D.vi. CYP3A4 and CYP3A5 Linkage Disequilibrium
CYP3A4 and CYP3A5 are found in a high degree of linkage disequilibrium in
Caucasian and Asian populations with the most common haplotype being
28 CYP3A4*1 and CYP3A5*3 [3]. Caucasians also exhibit linkage disequilibrium between CYP3A4*1B and CYP3A5*1 [100, 106]. It is hypothesized that the two haplotypes could result in similar activity; the two alleles compensate for one another. African populations have very diverse haplotypes with no significant degree of linkage disequilibrium seen [3], however, they are more likely to carry both the CYP3A4*1B and the CYP3A5*1 alleles [100, 166].
1.E. Specific Aims
The objective of this project is to determine the functional consequence of the
CYP3A4*1G genetic variant using in vitro methods. The specific goals of Aim 1 was to use immortalized human lymphocytes with differing CYP3A4 genotypes
(CYP3A4*1/*1, CYP3A4*1/*1G, and CYP3A4*1G/*1G) to analyze the effect of
CYP3A4*1G on relative mRNA content, protein content, and enzyme activity.
Aim 2 used human liver microsomes to, again, determine the effect of
CYP3A4*1G on protein content and enzyme activity.
29 Table 1.1. List of CYP2C9, CYP2D6, and CYP3A4/5 Common Substrates, Inhibitors, and Inducers
Substrates Inhibitors Inducers CYP2C9 ibuprofen amiodarone rifampin phenytoin fluconazole tolbutaminde fluvoxamine S-warfarin fluoxetine sulfaphenazole
CYP2D6 atomexetine bupropion bufuralol fluoxetine debrisoquine paroxetine desipramine quinidine dextromethorphan CYP3A4/CYP3A5 buspirone azamulin carbamazepine erythromycin clarithromycin phenytoin felodipine erythromycin rifampin dextromethorphan fluconazole St. John’s wort lovastatin grapefruit juice midazolam indinavir nifedipine itraconazole simvastatin ketoconazole terfenadine ritonavir testosterone saquinavir triazolam troleandomycin verapamil
This list is not intended to be inclusive. Adapted from FDA website “Drug Development and Drug Interactions: Table of Substrates, Inhibitors and Inducers.”
30 Table 1.2. Major CYP2C9 Alleles
Allele Nucleotide Change Protein Change Activity Change CYP2C9*2 (rs1799853) 430C>T R144C decreased CYP2C9*3 (rs1057910) 1075C>C I359L decreased Adapted from Fohner, et al, 2013 [90, 167, 168].
Table 1.3. CYP2C9 Allele Frequencies
Allele CSKT CEU YRI JPT CHB Canadian Canadian Central Inuit First America Nation (Tepehuano/ Mestizos) rs4918758 25.0 35.8 30.1 41.3 33.7 CYP2C9*2 5.17 10.4 0 0 0 0 3.0 1 - 7 CYP2C9*3 2.69 5.8 0 2.3 4.7 0 6.0 1.5 rs28371689 30.77 rs1057911 2.7 5.8 0 3.4 4.4 CSKT = Confederated Salish and Kootenai Tribes (n=188 chromosomes). HapMap populations: CEU = Utah residents with ancestry from northern and western Europe (n=120 chromosomes); YRI = Yoruba in Ibadan, Nigeria (n=120 chromosomes); JPT = Japanese in Tokyo, Japan (n=90 chromosomes); CHB = Han Chinese in Beijing, China (n=90 chromosomes). Adapted from Fohner, et al., 2013 [90].
31 Table 1.4. Major CYP2D6 Alleles
Allele Nucleotide Changes Protein Effect Phenotype CYP2D6*1 Wild-type None EM CYP2D6*2 2850C>T; 4180G>C R296C; S486T CYP2D6*3 2549delA Frameshift PM CYP2D6*4 100C>T; 1846G>A; 4180 G>C P34S; splicing defect; PM S486T CYP2D6*5 Gene Deletion Gene deletion PM CYP2D6*9 2615delAAG K281del IM CYP2D6*10 100C>T; 4180G>C P34S; S486T IM CYP2D6*17 1023C>T; 2850C>T Amino Acid Substitution IM CYP2D6*28 19G>A; 1704C>G; 2850C>T; V7M; Q151E; R296C; ND 4180G>C S486T CYP2D6*33 2483G>T A237S EM CYP2D6*35 31G>A; 2850C>T; 4180G>C V11M; R296C; S486T EM CYP2D6*41 2850C>T; 2988G>A; 4180G>C R296C; splicing defect; IM S486T Phenotypes denoted as: PM = Poor Metabolizer; IM = Intermediate Metabolizer. Adapted from Fohner, et al., 2013 [90].
32
84.9 7.10
17.8
- - 4.20 0 0 -
- 0.85 South South 0 America America 3.6 3.6 (Embera/ Mapuche) 39.9 39.9 1.80
.
99.4 4.60 [90]
19.4
- - 1.44 -
12.45 10.20
- 2.54 - - 0 Central Central America America 0 0 0.6 0.6 2013 Mestizos) 38.8 38.8 0.80
(Tepehuano/
et al., et
94.0
- 0 3.0 3.0 First First Nation Canadian Canadian 68.0 68.0
8.3
- 0 2.30 Inuit 89.10 6.7 6.7 Canadian Canadian
9.60 64.1 4.0
38.4
- - - 1.10 1.27 0.21 0 0 -
- - - 0 0 0 2.2 2.2 2.54 2.54 22.4 Chinese 18.0
93.8 7.2 2.60
45.9
- - - 0.77 0 0 0 0 0 0
-
- 0
4.1 8.6 27.0 27.0 0.51 Japanese
7.8
83.0 0.60 6.90 1.15 7.50 26.0 1.10 14.9
-
------African African 3.86 3.86 American 29.7 29.7 0.18 2.80 0.18 2.70 13.7 0.38 1.84
0
0 8. 8.5 33.4
83.0
14.
6.9 3.8
- -
3.20 1.11 0 - -
-
- - - - 0 0 0 0 0.9 4.8 6.9 6.9 11.3 European 28.6
CYP2D6Allele Frequencies 0 0.27 1.34 0.80 1.34 0.27 0.53 1.07 37.57 20.86 11.23 CSKT
Major Major
. 5
Table 1.5 Table Allele CYP2D6*1 CYP2D6*3 CYP2D6*4 CYP2D6* CYP2D6*9 CYP2D6*10 CYP2D6*17 CYP2D6*28 CYP2D6*33 CYP2D6*35 CYP2D6*41 CSKT = Confederated Salish and Kootenai Tribes (n=374 chromosomes). Adapted from Fohner, Fohner, from Adapted chromosomes). (n=374 Tribes Kootenai and Salish Confederated = CSKT
33
Table 1.6. Major CYP3A4 alleles
Allele Nucleotide Protein Effect Change in Change Activity CYP3A4*1B (rs2740574) -392A
Table 1.7. Major CYP3A4 Allele Frequencies
Allele CSKT CEU YRI JPT CHB Central America (Tepehuano/ Mestizos) CYP3A4*1B 2.20 3.0 72.0 0 0.3 8.0 – 8.8 CYP3A4*1G 26.81 8.3 88.9 29.7 28.0 CYP3A*2 0 0 0 0 0 CYP3A4*3 0 1.2 0 0 0 CYP3A4*8 0 0 0 0 0 CYP3A4*12 0 0 0 0 0 CYP3A4*13 0.60 0.4 0 0.6 1.2 CYP3A4*15A 0.68 0 2.84 0 0 CYP3A4*17 0 0 0 0 0 CYP3A4*18 0 0 0 1.7 0 CYP3A4*22 2.44 5.29 0 0 0 rs2687116 2.27 1.8 74.4 0 0 CSKT = Confederated Salish and Kootenai Tribes (n=188 chromosomes). HapMap populations: CEU = Utah residents with ancestry from northern and western Europe (n=120 chromosomes); YRI = Yoruba in Ibadan, Nigeria (n=120 chromosomes); JPT = Japanese in Tokyo, Japan (n=90 chromosomes); CHB = Han Chinese in Beijing, China (n=90 chromosomes). Adapted from Fohner, et al., 2013 [90, 121, 169].
34 Table 1.8. Major CYP3A5 Alleles
Allele Nucleotide Protein Effect Change in Activity Change CYP3A5*3 (rs776746) 6986A>G intronic nonfunctional CYP3A5*6 (rs10264272) 14690G>A truncation nonfunctional CYP3A5*7 (rs76293380) 27131_27132insT frameshift nonfunctional rs15524 31611T>C 3’ UTR Adapted from Fohner, et al, 2013 [90, 135, 168].
Table 1.9. Major CYP3A5 Allele Frequencies
Allele CSKT CEU YRI JPT CHB CYP3A5*3 92.27 94.1 15.0 73.3 66.3 CYP3A5*6 0 0 16.8 0.6 1.2 CYP3A5*7 0 0 0 0 0 rs15524 10.64 4.0 72.6 28.5 33.7 CSKT = Confederated Salish and Kootenai Tribes (n=188 chromosomes). HapMap populations: CEU = Utah residents with ancestry from northern and western Europe (n=120 chromosomes); YRI = Yoruba in Ibadan, Nigeria (n=120 chromosomes); JPT = Japanese in Tokyo, Japan (n=90 chromosomes); CHB = Han Chinese in Beijing, China (n=90 chromosomes). Adapted from Fohner, et al., 2013 [90, 121, 169].
35
Figure 1.1. Cytochrome P450 Cycle. Adapted from [170].
36
Exons are denoted by boxes Adapted Exonsdenoted introns are as by and lines.
.
CYP3A5
. B. . B.
CYP3A4
.
A. Map Gene SNP 2.2. Figure 168]. 135, [30, 2002 al., et Lamba, from
168] 135, [30, boxes by Exons denoted are .
2002
CYP3A5 et al.,
B. .
CYP3A4
dapted fromLamba,
A
2. SNP GeneMap A. .
2
. B. A Figure and introns as lines.
37
Chapter 2: Lymphocytes as Surrogates of CYP3A Drug
Metabolism
39 2.A. Introduction
CYP3A4 is responsible for the metabolism of 50% of drugs currently on the market [129, 142, 143]. It is reported that 66 - 90% of the interindividual variability in CYP3A4 activity is a result of genetic variability [154]. While there are many identified SNPs, several do not have conclusive evidence linking them to the interindividual change [168]. CYP3A4*1G is an SNP located within intron 10
(20230G>A) and has conflicting reports of its impact on CYP3A4 activity. Most reports show a decrease in activity [116-120], however, some show an increase in function [137-139].
Genetic variants can be measured in vivo, where CYP activity is measured via administration of probe drugs or radiolabeled drugs [171, 172]. Probe drugs are metabolized by a single drug-metabolizing enzyme and are administered to identify the function of that enzyme, or the change in function of the enzyme due to the presence of a SNP. Plasma concentrations of the probe drug and metabolites are measured to estimate various pharmacokinetic parameters [172].
Also, one could measure CYP changes in activity, protein, and mRNA levels within liver hepatocytes or microsomes after performing a liver biopsy [173, 174].
However, both methods present some considerable disadvantages. Probe drug administration creates the risk of adverse events, multiple blood draws and urine collection, length of time needed to collect samples from patient, and the associated high cost [175]. Liver biopsies are not routinely performed, especially for the purpose of the phenotyping a patient [173]. These substantial
40 disadvantages associated with in vivo phenotyping presents a large barrier in clinical practice as well as research.
Because of some of the challenges associated with existing phenotyping methods, there is increased interest in using a more readily available tissue such as peripheral blood cells. These cells could be used as surrogate markers of drug metabolism or changes in drug metabolism. Peripheral blood cells are made up of red blood cells, white blood cells, and platelets. White blood cells are made in lymphoid tissues and include several subsets of cells: neutrophils, eosinophils, basophils, monocytes, and lymphocytes. Lymphocytes are responsible for recognizing foreign antigens and mounting a response [176]. There are several advantages to measuring CYP activity in lymphocytes: no need for probe drug administration, less invasive sampling, less time involved for the patient, as well as less expensive to test [175]. Several CYPs have been measured in lymphocytes, including CYP3A4 [177]. However, there are conflicting reports in literature of the consistency in measuring CYP3A4 activity and mRNA levels and its correlation to liver activity and expression [173, 175, 178-183].
The goal of this study was to determine the functional consequences of the genetic variant CYP3A4*1G using lymphocyte cell lines that are wild type, heterozygote, and homozygote for the CYP3A4*1G genotype. CYP3A4*1G functional consequence in lymphocytes was assessed by measuring 1) mRNA by
41 quantitative PCR, 2) protein levels by immunoblot detection, and 3) CYP3A4 activity using the substrate luciferin IPA.
2.B. Materials and Methods
2.B.i. Cells
B-lymphocytes were ordered from the National Human Genome Research
Institute Sample Repository for Human Genetic Research through the Coriell
Institute for Biomedical Research. Lymphocytes were selected based on
CYP3A4 and CYP3A5 genotype: CYP3A4*1/*1, CYP3A4*1/*1G, and
CYP3A4*1G/*1G; all samples were CYP3A5*1/*1. All samples were from females. Cells were maintained in RPMI Medium 1640 with 2mM L-glutamine and 15% FBS at 37°C and 5% CO2. They were split to a density of 200,000 viable cells/mL every third day.
2.B.ii. RNA Isolation and cDNA synthesis
RNA was isolated using the PureLink® RNA Mini Kit (Life Technologies,
Carlsbad, CA) and RNA concentration and quality was measured on a Nanodrop spectrophotometer. PureLink® DNAse Treatment (Life Technologies) was used during RNA extraction to digest any DNA. cDNA was immediately synthesized using the High Capacity RNA-to-cDNA™ Kit (Applied Biosystems®, Carlsbad,
CA) on a C1000 Thermocyclyer (Bio-Rad, Hercules, CA). Samples were not frozen and immediately underwent quantitative PCR.
42 2.B.iii. Quantitative Real-time PCR
CYP3A4, CYP3A5, β-actin, and glyceraldehyde 3-phosphate dehydrogenase
(GAPDH) mRNA were measured using TaqMan® Gene Expression Assay
(Applied Biosystems®). Primer/probe pairs were: CYPA4 Hs00604506_m1;
CYP3A5 Hs01070905_m1; β-actin H99999903_m1; GAPDH Hs03929097_g1.
Taqman® probes had a reporter dye, FAM™, on the 5’ end and a nonfluorescent quencher (TAMRA™) on the 3’ end. TaqMan® Universal Master Mix II, with UNG was used to perform amplifications. TaqMan® probe identification and amplicon length as well as exon location are defined in Table 2.1. All amplicons produced are relatively short and are not expected to PCR efficiency. Probes for CYP3A4 and CYP3A5 span exons, so no genomic DNA will be amplified. cDNA template,
200 ng, underwent cycling conditions of 1 cycle at 50°C for 2 min, 1 cycle of 95°C for 10 min, then 50 cycles of denaturation at 95°C for 15 sec and annealing/elongation at 60°C for 1 min on an Mx3000P qPCR system (Agilent
Technologies, Santa Clara, CA). Each amplification was performed in quadruplicate at two separate times with independent RNA isolations. Data was normalized to housekeeping gene (ΔCt) by subtracting its Ct from the Ct of the
CYP3A4/5. Ct was defined as 10 standard deviations above average background level. Reporter signal was normalized to a passive reference dye, ROX™, included in the master mix.
43 2.B.iv. CYP3A4 Protein Quantitation in Lymphocytes
Protein quantitation in lymphocyte was performed by immunoblot. Total protein
(10 µg) of cell lysate was added and resolved on a Tris-Hepes NH 4-20%
(NuSep, Lane Cove, Australia). Protein was transferred to nitrocellulose membranes and blocked with 5% milk for 1 hour. The membrane incubated with primary anti-CYP3A4 antibody (Abnova, Taipei City, Taiwan) (1:1,000 dilution) on a rocker overnight at 4°C. Secondary anti-mouse antibody was added (1:25,000 dilution) for 1 hour at room temp. Membrane was developed using the West
Femto Kit (Thermo Scientific™, Waltham, MA) and imaged on a using an LAS-
3000 camera (Fujifilm, Minato, Tokyo).
2.B.v. CYP3A4 Protein Quantitation in Human Liver Microsomes
Protein quantitation in human liver microsomes was performed by immunoblot.
Total protein (10 µg) were added per HLM sample and resolved on a Tris-Hepes
NH 4-20% gel (NuSep) Protein was transferred to nitrocellulose membranes and blocked with 5% milk for 1 hour. The membrane incubated with primary anti-
CYP3A4 antibody (Abnova) (1:1,000 dilution) on a rocker overnight at 4°C.
Secondary anti-mouse antibody was added (1:25,000 dilution) for 1 hour at room temp. Membrane was developed using the West Femto Kit (Thermo Scienctific™) and imaged on an LAS-3000 camera (Fujifilm).
44 2.B.vi. CYP3A4 Activity in Lymphocytes
CYP3A4 activity was assessed in lymphocytes using P450-Glo™ assay system
(Promega, Madison, WI). Lymphocytes were counted in serum-free media and 5 x 104 – 5 x 106 cells in suspension were placed into 1.5 ml eppendorf tubes. An equal volume of 2x P450-Glo™ substrate (Lucierin IPA) in serum free media was added to cell suspension. Tubes were incubated at 37°C and 5% CO2. An equal volume of Luciferin Detection Reagent was added and tubes were placed on an orbital shaker for 15-20 min at room temp. Solution was transferred to a white, untreated 96-well plate and luminescence detected on a SynergyMX microplate reader (Biotek, Winooski, VT) with an integration time of 1 second/well.
2.B.vii. Data Analysis
A Tukey’s post hoc test was completed to determine differences in the qPCR
ΔCts between lymphocyte cell lines, α=0.05 (KaleidaGraph, Reading, PA).
2.C. Results
2.C.i. CYP3A4 mRNA Expression in Lymphocytes
All genes of interest were detected in all three lymphocyte cell lines, although,
CYP3A4 mRNA levels were very low as demonstrated by the high Ct values
(Table 2). Representative quantitative PCR traces of each cell line are shown in
Figure 2.1. The Cts of all genes are reported in Table 2.2 and ΔCts, CYP3A4 and
CYP3A5 normalized to housekeeping genes, are reported in Figure 2.3. ΔCts show that the order of the CYP3A4 expression is as follows: CYP3A4*1/*1G >
45 CYP3A4*1G/*1G > CYP3A4*1/*1. There is statistical significance between all three cell lines (normalizing to β-actin: *1/*1 and *1/*1G p<0.0001; *1/*1 and
*1G/*1G p=0.002; *1/*1G and *1G/*1G p=0.031 and normalizing to GAPDH:
*1/*1 and *1/*1G p<0.0001; *1/*1 and *1G/*1G p=0.045; *1/*1G and *1G/*1G p=0.002). With respect to CYP3A5 expression levels, CYP3A4*1/*1G also expresses the most CYP3A5. The *1/*1G genotype expresses more CYP3A5 than the *1/*1 genotype, after normalizing to both β-actin and GAPDH, p<0.0001 for all comparisons. However, there is no significance between the CYP3A5 expression levels when controlling for either housekeeping gene between the
CYP3A4*1G/*1G and CYP3A4*1/*1 genotypes (β-actin p=0.63; GAPDH p=0.4).
To show that CYP3A4 was amplified in qPCR, and the high Ct was not due to primer/probe degradation, the amount of cDNA added to each reaction was diluted. Instead of adding 200 ng of total RNA to each reaction, 25 ng of total
RNA was added. Figure 2.3 shows the qPCR traces of the template dilution. The diluted total RNA samples have a higher Ct value indicating less CYP3A4 mRNA in the sample. Table 6 shows the Ct values of each gene for the different amounts of total RNA added as well as the ratio of Cts for 200 ng to 25 ng. These ratios were similar across genes, confirming that the Cts measured are not due to probe degradation.
46 2.C.ii. CYP3A4 Protein Expression in Lymphocytes
Immunoblots of each lymphocyte cell line were performed to quantify CYP3A4 protein content. In contrast to the qPCR data, western blot quantification (Figure
2.4) shows that CYP3A4*1G/*1G has the most expression, followed by
CYP3A4*1/*1G. Wild-type CYP3A4 had no detectable protein, however, a high molecular weight band was detected. To understand this high molecular weight band, we ran another immunoblot in human liver microsomes of the same genotypes (Figure 2.4). The high molecular weight band was not detected in any genotype of the human liver microsomes and it appears to be specific to lymphocytes.
2.C.iii. CYP3A4 Activity in Lymphocytes
Next, CYP3A4 activity in lymphocytes was measured. Cells were first incubated with 3 µM substrate for 15 min with zero activity measured. Next, time was increased to 1 hour incubation at the same substrate concentration and, again, measured zero activity. Finally, cells were treated with 25 µM substrate for 3 hours; the rate measured was negligible.
2.D. Discussion
There is interest in finding a more accessible tissue as a surrogate of drug metabolism in the liver. Blood sampling is minimally invasive, low risk, and relatively unlimited. However, CYPs are not highly expressed within lymphocytes and there are several contradicting studies as to their metabolic relevance. Our
47 goal was to establish whether immortalized lymphocytes, with defined CYP3A4 and CYP3A5 genotypes, could be used to determine the functional consequence of the CYP3A4*1G SNP.
Several groups have been able to quantify CYP3A4 in lymphocytes. Sempoux et al. and Starkel et al. showed that CYP3A proteins were detectable in B- lymphocytes by immunoblot and or immunohistochemistry [184, 185]. Nakamoto et al. were able to detect CYP3A4 mRNA in all samples of lymphocytes (n=8) by quantitative competitive (QC) RT-PCR. They were also able to measure a statistically significant level of induction after oral administration of rifampin.
Along with an increase of mRNA content, this group also found an increase in the ratio 6β-hydroxycortisol to cortisol, indicating an increase in CYP3A4 liver activity.
These data imply that lymphocytes can indeed be used as a surrogate for liver activity [180]. However, this group did not use primers that spanned exons, causing amplification of any DNA present in the sample. This could result in more
CYP3A4 measured than is actually present as cDNA. Krovat et al. and
Nowakowski-Gashaw et al. were able to detect CYP3A4 in lymphocytes using
QC-PCR [182, 186]. Gashaw et al. was also able to measure CYP3A4 expression in all samples and also found a weak, but significant, correlation between mRNA content in lymphocytes with alprazolam clearance [173].
Some groups have been able to detect CYP3A4 but were unable to correlate levels of expression with CYP3A4 function in the liver. Finnstrom et al. reported
48 measuring CYP3A4 expression at relatively high levels. They used qPCR with
16.7 ng total RNA and a Ct cutoff value of 38, which corresponds to 1000 molecules. Most lymphocyte traces had Cts between 33 and 37, however, they did not show housekeeping, normalizing gene traces. They were unable to find a correlation between liver and lymphocyte CYP3A4 expression [179]. Haas et al. also were able to measure CYP3A4 mRNA in all samples but found a poor correlation between lymphocyte mRNA and total body enzyme function. However,
Koch et al. and Siest et al. were unable to detect CYP3A4 in lymphocytes and used up to 40 ng of total RNA per reaction [178, 181]. Several of these studies measured CYP3A4 in lymphocytes after induction. Weak correlation, if any was found, possibly due to the mechanism of induction. Rifampin activates pregnane
X receptor (PXR), which binds to the promoter of CYP3A4. However, PXR expression is tissue-specific and also exhibits interindividual variability [175]. This could explain the negative findings after induction. None of these studies investigated the impact of CYP3A4 genotypes and its expression in lymphocytes with CYP3A4 liver function.
We were able to measure the relative quantities of CYP3A4 in all lymphocyte cell lines. The Ct values of CYP3A4 indicate very low levels present in lymphocytes.
CYP3A4*1/*1G heterozygotes had not only the most CYP3A4 expression, but also the most CYP3A5 expression, which all had the same genotype. Therefore,
CYP3A4 content between cell lines, although significant, but doesn’t appear to be meaningful. One would expect to see a gene-dose relationship between cell
49 line with the heterozygote cells expressing an intermediate level of mRNA, making it impossible for the heterozygotes to express the most CYP3A4.
Therefore, there is some other unknown mechanism, other than the CYP3A4*1G genotype, causing the CYP3A4*1/*1G cells to express the most CYP3A4 and
CYP3A5. Perhaps, there are unknown SNPs within the regulation pathway of
CYP3A enzymes that is causing the upregulation of both CYP3A4 and CYP3A5, for instance, PXR.
We are confident that all Cts measured are real and not due to primer/probe degradation. Figure 2.3 shows the qPCR curve shift to the right when less total
RNA was added. Also, the ratio Cts of the 200 ng total RNA to 25 ng total RNA is similar across all genes. Again, indicating that measured Cts are of templates and changes in Cts are due to changes in starting template.
In contrast to the qPCR data, protein quantification by immunoblot actually shows
CYP3A4*1G/*1G has the highest protein content, followed by CYP3A4*1/*1G, and not detectable in CYP3A4*1/*1. This data is more in line with the gene-dose theory with the heterozygotes expressing an intermediate amount of protein.
Interesting, the wild-type lymphocytes exhibit a larger molecular weight band that is present at lower levels in the heterozygote and not present in the *1G homozygote. This led us to perform another immunoblot with a more relevant tissue, human liver microsomes (HLM), to see if this high molecular weight band is also present. This band was not seen in any genotype of HLM samples and
50 appears to be specific to the lymphocytes. It is unclear at this time what this band is or its clinical relevance, if any. However, it is clear that it is not due to the gene status of CYP3A4.
We were unable to measure the activity of CYP3A4 within lymphocytes using a highly sensitive, luminescent method. Cells are incubated with luciferin IPA, which creates luminescence after it is metabolized by CYP3A4, upon addition of the detection reagent. This is a sensitive method to detect minimal activity within the lymphocytes. However, regardless of length of incubation or the concentration of substrate, negligible, if any, activity was measured.
Our study shows that lymphocytes do not appear to be a good source to measure CYP3A4 activity in vitro. Our goal was to use lymphocytes as a surrogate human tissue to evaluate the effects of the CYP3A4*1G SNP, however, measurements of CYP3A4 mRNA and protein content and CYP3A4 activity in lymphocytes suggest that they are not a reliable surrogate for liver enzyme content and function. Our data confirm that lymphocytes are not able to be used to identify changes in enzymes due to CYP3A genetic variation.
51 Table 2.1. TaqMan® Probe Information
Protein Amplicon Length Location (exons) CYP3A4 119 2-3 (Hs00604506_m1) CYP3A5 101 2-3 (Hs01070905_m1) β-actin 171 1 (Hs99999903_m1) GAPDH 58 8 (Hs03929097_g1) Primer/probes pairs where ordered from Applied Biosystems®. TaqMan® Gene Expression Assay was used with a reporter dye, FAM™ on the 5’ end and a nonfluorescent quencher (TAMRA™) located on the 3’ end.
52 Table 2.2. Cts by Genotype
CYP3A4 CYP3A5 β-Actin GAPDH CYP3A4*1/*1 38.95 ± 0.616 34.02 ± 0.189 14.81 ± 0.147 15.84 ± 0.167 37.68 ± 0.259 33.85 ± 0.250 13.54 ± 0.015 14.76 ± 0.059 CYP3A4*1/*1G 38.33 ± 0.288 33.06 ± 0.126 16.68 ± 0.224 16.51 ± 0.213 36.64 ± 0.387 30.61 ± 0.214 13.84 ± 0.282 14.86 ± 0.577 CYP3A4*1G/*1G 38.85 ± 0.315 34.80 ± 0.355 16.07 ± 0.125 16.32 ± 0.200 37.29 ± 0.367 34.27 ± 0.161 14.05 ± 0.139 14.78 ± 0.154 Two qPCR runs were completed per cell line (each row represents one run). Ct is defined as ten standard deviations above the average background level.
53 Table 2.3. Cts of Template Dilution
CYP3A4 CYP3A5 GAPDH β-actin 200 ng 38.91 ± 0.54 34.32 ± 0.28 15.79 ± 0.11 20.36 ± 0.11 25 ng 44.68 ± 1.60 39.46 ± 0.84 20.05 ± 0.21 24.89 ± 0.09 Ratio 200 ng/25 ng 0.871 0.870 0.788 0.818 Each qPCR run in quadruplicate. Ct is defined as ten standard deviations above the average background level.
54
A. B. CYP3A4*1/*1 CYP3A4*1/*1G
4 4
CYP3A4 CYP3A4 CYP3A5 CYP3A5 b-Actin b-Actin GAPDH GAPDH 3 3
2 2
1 1 Fluorescence (dRn) Fluorescence (dRn) Fluorescence
0 0
-1 -1 0 10 20 30 40 50 60 0 10 20 30 40 50 60
Cycle Number Cycle Number C. CYP3A4*1G/*1G
4 CYP3A4 CYP3A5 b-Actin GAPDH 3
2
1 Fluorescence (dRn) Fluorescence
0
-1 0 10 20 30 40 50 60
Cycle Number
Figure 2.1. qPCR Traces of Lymphocytes. A. CYP3A4*1/*1 lymphocytes B. CYP3A4*1/*1G lymphocytes C. CYP3A4*1G/*1G lymphocytes. Traces (n=4) are representative. Solid circles are CYP3A4 amplicons, empty circles are CYP3A5 amplicons, solid triangles are β-actin amplicons, and empty triangles are GAPDH amplicons. Fluorescence (dRN) is the reporter signal normalized to a passive reference dye, ROX™.
55 Delta&Ct&by&Genotype
30 CYP3A4 - b-Actin CYP3A4 - GAPDH CYP3A5 - b-Actin CYP3A5 - GAPDH 25
20
15 delta&Ct&
10
5
0 CYP3A4*1/*1 CYP3A4*1/*1G CYP3A4*1G/*1G Figure 2.2. CYP3A4 and CYP3A4 Normalized Expression Levels. Each gene is normalized to housekeeping genes, as denoted in the figure legend. Lower values correlate to more mRNA expression of the gene of interest (CYP3A4 or CYP3A5).
56 CYP3A4*1G/*1G
3.5 200 ng 25 ng 3
2.5
2
1.5
1 Fluorescence (dRn) Fluorescence
0.5
0
-0.5 0 10 20 30 40 50 60
Cycle Number
Figure 2.3. qPCR Traces of Template Dilution. qPCR traces of 200 ng (solid circles) and 25 ng (empty circles) of total RNA added per reaction. Fluorescence (dRN) is the reporter signal normalized to a passive reference dye, ROX™.
57 A. 1 2 3 4 5 6 7 8 9 10
B.
Immunoblot Quantification 0.5
0.4
0.3
ng CYP3A4 ng 0.2
0.1
0 CYP3A4*1/*1 CYP3A4*1/*1G CYP3A4*1G/*1G
C. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Figure 2.4. Immunoblot and Quantification CYP3A4 Protein. A. Lymphocyte CYP3A4 immunoblot. Standards lane 1 (10 ng), 2 (5 ng), 3(1 ng), 4 (0.5 ng), 5 (0.25 ng). Lymphocytes lanes 7 (CYP3A*1G/*1G), 8 (CYP3A4*1/*1G), and 9 (CYP3A4*1/*1). Negative control, Sf9 insect cells, lane 10.. B. CYP3A4 protein quantitation stratified by genotype of lymphocyte cell lines. C. HLM CYP3A4 immunoblot. Lanes 1 and 2 are CYP3A4*1G/*1G samples, lanes 3-7 are CYP3A4*1/*1G samples and lanes 8-11 are CYP3A4*1/*1 samples. Standards are in Lanes 13(1 ng), 14 (5 ng), and 15 (10 ng).
58
Chapter 3. Effect of the Genetic Variant CYP3A4*1G in Human Liver Microsomes
59 3.A. Introduction
CYP3A4 is drug-metabolizing enzyme highly expressed in the liver and intestine
[97]. CYP3A4 metabolizes about 50% of all medications available on the market, comprised of more than 120 different drugs from therapeutic classes that include antihistamines, immunosuppressive agents, benzodiazepines, and HIV protease inhibitors [99, 131]. Up to 40-fold variability has been reported in interindividual
CYP3A4 activity [112, 155, 156] and up to 90% is predicted to be due to genetic factors [154]. Because of the huge diversity of CYP3A4 substrates, genetic variability can have a large clinical impact in drug response and toxicity [30].
Pharmacogenomic studies aim to identify these genetic variations and determine their resulting changes in activity.
Probe drug studies are the gold standard in in vivo phenotyping of pharmacogenetic variation as they provide direct predictions of alterations in pharmacokinetic parameters, however, these studies have considerable disadvantages (discussed earlier in Chapter 1 section A.ii.). Therefore, investigators also try to evaluate pharmacogenetic variation using in vitro methods [187, 188]. One of these widely accepted methods is using human liver microsomes (HLM) [189]. HLMs are generated from liver samples, obtained by liver biopsies or organ donation. Liver samples undergo several cycles of homogenation and centrifugation to form vesicles from isolated endoplasmic reticulum. Because CYPs are microsomal proteins, HLMs contain concentrated levels of CYPs. Liver microsomes enable one to make pharmacokinetic
60 estimations (rate of metabolism, intrinsic clearance) that are scalable to whole liver and whole body predictions, without the need to administer medications in vivo and gather blood and urine samples. For instance, HLMs were first identified to give good predictions of intrinsic clearance in rats [41, 190-192]. Correlations were soon made between clearances measured in HLMs and those measured in an in vivo pharmacokinetic study [187, 193, 194]. In order to best estimate intrinsic clearance, it is important to maintain incubations conditions similar to those seen in vivo. Specifically, incubation times need to be such that product formation is linear (initial rate is constant) over the duration and drug concentrations are comparable to clinical concentrations [187]. Scaling from
HLMs to whole body deviate the most when metabolism occurs in organs other than the liver, for instance, the intestines, or when metabolism occurs by routes other than CYP oxidation [187, 195]. This scenario would lead to an underestimate of total clearance [187]. HLMs are commonly used in research as well as drug development [196]. They are inexpensive, convenient, readily available, and create reproducible data [197]. When stored at -80°C, microsomes retain their activity for years [198].
There are disadvantages to using microsomes as an alternative for in vivo data.
Disease and medication histories of the subjects from whom the livers are obtained are often unknown or incomplete. Disease state, such as diabetes mellitus, can cause decreased CYP3A4 expression and activity [199]. Length of time before organ procurement can also affect CYP expression; increased
61 cytokine release, due to inflammation and infection, as well as decreased hormone secretion leads to down regulation of CYPs [30, 154, 200].
Concurrent medications can cause inhibition or induction and need to be incorporated into data analysis. Also, organ collection protocols, tissue storage conditions, and microsomal preparations all can have an effect on microsomal enzyme activity. Because of this, it has reported that in vitro interindividual variability is larger than in vivo interindividual variability [129].
Our study aimed to investigate the any change in activity due to the CYP3A4*1G genetic variant in vitro using HLMs that have been previously genotyped.
CYP3A4 activity in HLM was measured using a selective CYP3A4 substrate with minimal/no substrate overlap with CYP3A5. Our goal was to make genotype- phenotype associations in the HLMs to assess CYP3A4*1G activity.
3.B. Materials and Methods
3.B.i. Human Liver Microsomes
Human liver microsomes (n=324) were obtained from University of Washington,
School of Pharmacy Human Liver Tissue Bank (Seattle, WA). The University of
Washington made all human liver microsomes. Liver tissue was homogenized using a Bead Ruptor. Homogenate was centrifuged at 15,000g for 30 min.
Centrifuge supernatant at 120,000g for 70 min. The pellet was resuspended in wash buffer and homogenized with a glass homogenizer and centrifuged at
120,000 g for 70 min. Pellet resuspended in wash buffer with a glass
62 homogenizer and stored at -80°C. Livers were genotyped in Debbie Nickerson’s
Laboratory in the Department of Genome Sciences University of Washington
(Seattle, WA). Genotyping was determined via PGRNseq Platform (University of
Washington). PGRNseq is a next-generation sequencing platform that sequences coding regions, adjacent noncoding regions, 2kb upstream, and 1kb downstream in 84 genes Very Important Pharmacogenes.
3.B.ii. CYP3A4 Activity in Human Liver Microsomes
CYP3A4 activity in HLMs (n=64) was determined with the P450-Glo™ kit
(Promega) according to manufacturer’s specifications. Five µg of total protein were preincubated in microfuge tubes with 4x P450-Glo™ substrate (luciferin
IPA) and KPO4 buffer for 10 min in a water bath at 37°C. An equal volume of
2mM NADPH was added to initiate the reaction and HLMs were, again, incubated for 10 min in a water bath at 37°C. An equal volume of Luciferin
Detection Reagent was added and tubes were placed on an orbital shaker for 15-
20 min at room temp. All incubations were completed in triplicate. Pooled HLMs were used as a positive control in every incubation set (in duplicate). Solution was transferred to a white, untreated 96-well plate and luminescence detected on a SynergyMX microplate reader (Biotek, Winooski, VT) with an integration time of
1 second/well. Optimization of HLM incubation conditions was completed using pooled HLM (Xenotech, Lenexa, KS).
63
3.B.iii. CYP3A4 Protein Quantitation in Human Liver Microsomes
Protein quantitation was performed by immunoblot. Total microsomal protein (10
µg) was resolved on a Tris-Hepes NH 4-20% gel (NuSep). Protein was transferred to nitrocellulose membranes and blocked with 5% milk for 1 hour. The membrane incubated with primary anti-CYP3A4 antibody (Abnova) (1:1,000 dilution) on a rocker overnight at 4°C. Secondary anti-mouse antibody was added
(1:25,000 dilution) for 1 hour at room temp. Membrane was developed using the
West Femto Kit (Thermo Scientific™) and imaged on an LAS-3000 camera
(Fujifilm).
3.B.iv. Data Analysis
An ANOVA was performed to test the differences between the HLM genotypes in both activity and protein quantitation, α=0.05 (KaleidaGraph). A multivariable regression was completed to test the significance of each variable on CYP3A4 activity. Variables tested were: protein content, genotype, age, gender, race, taking a 3A4 inducer, taking a 3A4 inhibitor, liver pathology, cause of death, height, weight, ICU time, liver ischemia, organ trauma, smoking status liver lab results, and pathology. Missing data was not included in the analysis (StatsPlus,
Alexandria, VA)
64
3.C. Results
3.C.i Optimization of Microsomal Incubation Conditions
HLMs are precious samples, some with extremely limited protein, so we optimized the HLM assay using commercially available pooled HLMs (Figure 3.1).
First, microsomal protein per incubation was titrated to determine the linear range of product formation (Figure 3.1A and 3.1B); we found that product formation was linear between 1.25 and 10 µg total protein per reaction. The substrate, luciferin
IPA, is provided from Promega in 100% DMSO, however, DMSO is a known inhibitor of CYPs [201]. Therefore, we next optimized DMSO concentration over a range of substrate concentrations (Figure 3.1C) and found the largest response using a substrate concentration of 8 µM in 0.25% DMSO. Finally, we optimized for time to assure linear product formation (Figure 3.1D) and found that product formation is linear between 5 and 20 min. We concluded from the optimization experiments that incubations with liver bank HLMs be carried out with 5 µg total protein and 8 µM final substrate concentration for 10 min.
3.C.ii. Subject Demographics
Demographics are listed in Table 3.1. Males made up 56.8% and Caucasian was the most common ethnicity at 95.4%. Other ethnicities are reported at less than
3%. At the time of writing this thesis, the only data received on genotypes was for
64 livers; 48 are wild-type (75%), 14 are heterozygote (21.9%), and 2 are homozygote for the *1G SNP (3.1%). Medications were not reported for all
65 samples, however, 12 were reported to be taking a CYP3A4 inducer (3.7%) and
18 were reported to be taking a CYP3A4 inhibitor (5.6%).
3.C.iii. Analysis of CYP3A4 Activity
Total CYP3A4 metabolic rate ranged from non-detectable to 10.3 pmol/µg protein/min (Figure 3.2) in 324 livers, demonstrating a huge variability in the
CYP3A4 activity. Pooled HLMs were used as a positive control. The average rate is 1.64 ± 0.30 pmol/µg protein/min, making the interday coefficient of variation
18%.
The CYP3A4 metabolic rates of 64 genotyped livers are stratified by genotype:
CYP3A4*1/*1 (n=48), CYP3A4*1/*1G (n=14), and CYP3A4*1G/*1G (n=2) (Figure
3.3). There was no statistical difference between any genotypes (p=0.519), however, there was a trend towards increased activity in CYP3A4*1G homozygous individuals, although numbers were small.
A linear regression analysis was performed with all data/variables reported from the liver bank with the HLMs. All variables, including genotype and gender, did not significantly impact the CYP3A4 rate.
3.4. Discussion
The demographics of the liver samples are not very diverse. The majority of the samples are Caucasian (95.4%), male (56.8%), and procured from patients who
66 were between the ages of 40 and 59 (32.4%) (all are independent variables).
There are few samples that are classified as Black (n=5), Asian (n=1), and
Hispanic (n=2). Therefore, it is difficult to tease out an effect from ethnicity due to limited sample number in this liver bank. Patients on medications that induce or inhibit CYP3A4 were also few (n=7 and 5, respectively), again, making it difficult to find significance on its effect on CYP3A4 metabolism. Patients classified as taking medications that induce CYP3A4 were taking, phenytoin, a strong inducer, and patients taking CYP3A4 inhibitors were taking amiodarone and cyclosporine, both weak inhibitors, and erythromycin, a moderate inhibitor. No donors were taking a strong inhibitor. There were several samples that had no report of medications, reporting of medications could have accounted for some interindividual variability. Regardless, all variables reported had no significant effect on the measured CYP3A4 metabolism rate in human liver microsomes.
This was a bit unexpected as it is reported that females can express up to 2-fold higher levels of CYP3A4 in vivo [136]. Also, there appears to be a trend that liver microsomes with higher protein contents tend to have a higher CYP3A4 metabolism rate, even after normalized to protein content (Figure 3.5). Although, again, this is statistically insignificant when analyzed in a multivariable regression.
We next further examined the effect of only the CYP3A4*1G genotype on
CYP3A4 metabolism. The average rate was determined of each genotype and an ANOVA was performed to identify if there was any difference between the three average rates. This, too, was not statistically significant (p=0.51898).
67 However, there is a trend toward increasing function of the CYP3A4*1G genotype. With only 64 genotyped HLMs, only two were identified as
CYP3A4*1G/*1G. When the rest of the liver bank is genotyped and included in the statistical analysis, there should be more homozygote *1G samples identified, resulting in an increase in statistical power. This may help determine if there is truly an increase in enzyme activity due to CYP3A4*1G genotype.
Our current data do not allow us to make a definite prediction of CYP3A4*1G impact on CYP3A4 metabolism. There was no statistical significance between
CYP3A4*1G genotype and CYP3A4 expression or activity. However, at the writing of this thesis, of the 324 microsomes with activity data, we only have genotypes reported for 64 microsomes. When the genotypes of the other 260 genotypes are incorporated in the analysis, perhaps significance can be teased out with the higher sample numbers and higher power. Until then, our data suggest that CYP3A4*1G has little significance on total metabolism and does not account for measured interindividual variability.
68 Table 3.1. Human Liver Microsome Demographics
Percentage n Gender Male 56.8% 184 Female 42.0% 136 Unreported 1.2% 4 Ethnicity Caucasian 95.4% 309 Black 2.8% 9 Hispanic 0.6% 2 Asian 0.3% 1 Unknown 0.9% 3 Age 0 – 19 22.2% 72 20 – 39 17.3% 56 40 – 59 32.4% 105 60 – 79 20.1% 65 80 – 99 1.5% 5 Unknown 6.5% 21 Genotype CYP3A4*1/*1 14.5% 47 CYP3A4*1/*1G 4.3% 14 CYP3A4*1G/*1G 0.6% 2 Unknown 80.6 261 CYP3A4 Inducers No 39.8% 129 Yes 3.7% 12 Unknown 56.5% 183 CYP3A4 Inhibitors No 38.3% 124 Yes 5.6% 18 Unknown 56.2% 182
Human liver microsomes demographics reported from human liver bank (University of Washington) (n = 324).
69 A. B.
HLM Protein Content Titration HLM Linear Protein Content
y = -843.65 + 1306.8x R2= 0.99338 1.4 104 1.4 104
1.2 104 1.2 104
1 104 1 104
8000 8000 RLU RLU 6000
6000 4000
4000 2000
2000 0
-2000 0 0 2 4 6 8 10 12 0 2 4 6 8 10 12
Protein Content (ug/reaction) Protein Content (ug/reaction)
C. D.
0.25% DMSO 0.5% DMSO 1% DMSO
DMSO Titration Time Curve
6 3 y = 0.35878 + 0.10042x R2= 0.99507
5 2.5
4 2
3 1.5
2 1 Rate (pmol/ug protein/min) (pmol/ug Rate Rate (pmol/ug protein/min) (pmol/ug Rate
1 0.5
0 0 0 5 10 15 20 25 30 35 0 5 10 15 20 25
Substrate Concentration (uM) Time (min) Figure 3.1. Optimization of CYP3A4 Activity in Pooled Human Liver Microsomes. A. Titration curve of total protein content of pooled. B. Linear range of total protein content titration curve. C. DMSO titration. D. Time curve completed to determine linear range of product formation.
70 CYP3A4 Interindividual Variability
12
10
8
6
4 Rate (pmol/ug protein/min) (pmol/ug Rate
2
0
Figure 3.2. CYP3A4 Interindividual Variability Measured in Human Liver Microsomes. 5 µg of liver microsome total protein was incubated with 8 µM luciferin IPA for 10 minutes. Detection reagent was added and luminescence was quantitated on a microplate reader (n=324).
71
CYP3A4 Activity
4
3.5
3
2.5
2
1.5 Rate (pmol/ug protein/min) (pmol/ug Rate
1
0.5
0 CYP3A4*1/*1 CYP3A4*1/*1G CYP3A4*1G/*1G Positive Controls
Figure 3.3. CYP3A4 Activity in Human Liver Microsomes. HLMs were stratified by CYP3A4 genotype (CYP3A4*1/*1 n=48; CYP3A4*1/*1G n=14; CYP3A4*1G/*1G n=2). Positive controls were run in duplicate. An ANOVA found no statistical differences between genotype (p=0.51898).
72 A A. Human Liver Microsomes
12
10
8
6
4 Rate (pmol/ug protein/min) (pmol/ug Rate
2
0
0 2 4 6 8 10 12 14 16
A Protein Content (ug/mL) B. Human Liver Microsomes
4
3.5
3
2.5
2
1.5 Rate (pmol/ug protein/min) (pmol/ug Rate 1
0.5
0
0 2 4 6 8 10 12 14 16
Protein Content (ug/mL)
Figure 3.5. Relationship Between Human Liver Microsome Total Protein Content and CYP3A4 Rate. A. Although there is no significant statistical relationship between total protein content and CYP3A4 metabolism rate, there is a trend between higher protein content and increased CYP3A4 rate. (n=324) B. Data points with rates greater than 5 pmol/ug protein/min were removed (n=322) (R=0.43).
73
Chapter IV: Summary and Future Directions
74 The goal of my research project was to decipher the functional consequence of the genetic variant CYP3A4*1G. I used in vitro models to assess CYP3A4 mRNA and protein expression and activity in lymphocyte cells and human liver microsomes.
We hoped to use lymphocyte cell lines, with the genotypes CYP3A4*1/*1,
CYP3A4*1/*1G, and CYP3A4*1G/*1G, as surrogates of CYP3A4 drug metabolism. This convenient, limitless resource with known genotypes would have been ideal to study the SNP of interest. Also, the ability to quantitate
CYP3A4 mRNA, protein, and activity could have huge implications in clinical field with the ability to phenotype patients with a single blood draw. Despite using a sensitive, luminescent detection method to measure CYP3A4 activity, we were unable to observe CYP3A4 activity in the lymphocytes. Therefore, lymphocytes do not make good surrogate markers for drug metabolism and cannot be used to measure a patients’ CYP3A4 drug metabolism rate and to make any conclusions as the functional consequence of the CYP3A4*1G genetic variant.
The second part of my research involved using human liver microsomes to assess the functional consequence of CYP3A4*1G variant. CYP3A4 activities were measured in 324 human liver microsomes (HLMs). At the time of writing this thesis, only 64 of these HLMs have been genotyped: 48 were homozygous wild-type CYP3A4*1/*1, 14 heterozygous CYP3A4*1/*1G, and 2 homozygous for
CYP3A4*1G/*1G. Although a trend was observed towards increased activity in
75 the CYP3A4*1G containing livers, a multivariable regression and ANOVA both found no significance of the CYP3A4*1G genetic variant. Until the genotypes of the remaining HLMs are known, there is not enough power to determine the effect of CYP3A4*1G.
In addition to incorporating the genotype of the remaining livers into the analysis, we will also normalize to the CYP3A4 protein levels, quantified by mass spectrometry, both completed at the University of Washington, as soon as the data collection is completed. However, in future work, I would measure the protein content of known CYP3A4 transcription factors, pregnane X receptor, constitutive androstane receptor, retinoid receptor, and hepatocyte nuclear factor in the human liver microsome samples [202, 203]. This would allow us to account for any of the CYP3A4 interindividual variability due to increased or decreased expression of transcription factors and may help us better ascertain any changes in function due to genetic variations in CYP3A4.
Other studies have found that CYP3A4*1G either causes increased [137, 138,
140] or decreased [116, 118-120] clearance in vivo. These mixed results could arise from the contribution of other CYP3A proteins. CYP3A7 may have minor impact as it is only expressed in 10% of adults, however, it can have large impact in CYP3A drug metabolism in those individuals as CYP3A7 has been reported to contribute up to 40% of CYP3A content [145-147]. CYP3A5 genotype could play a larger role in interindividual variability in CYP3A drug metabolism. In those who
76 express CYP3A5, CYP3A5 makes up 50% of CYP3A content [100]. Because of the linkage disequilibrium found between CYP3A4*1G and CYP3A5*1 found in
HapMap populations, it is difficult to identify the functional consequence of
CYP3A4*1G. However, a novel break in the linkage of CYP3A4*1G and
CYP3A5*3 has been uncovered in the Confederated Salish and Kootenai Tribes.
Due to the high CYP3A5*3 allele frequency in this population, many would carry both the CYP3A4*1G allele and the CYP3A5*3 allele. Studying this population, would allow us to assay for changes in CYP3A4 drug metabolism without the contribution from CYP3A5. Therefore, our laboratory has a proposed study to evaluate the CYP3A4*1G genetic variant in the CSKT population using an oral, subtherapeutic dose of midazolam, a CYP3A4 probe drug. Blood and urine will be collected to measure midazolam and its metabolite over time. This study will shed light on the CYP3A4*1G functional consequence unlike any other study has been able to do; we will be able to unequivocally assay for CYP3A4*1G metabolism.
In conclusion, changes in the CYP3A4 metabolism rate due to the *1G SNP can have wide implications, especially in Japanese, Chinese, and CSKT populations, where the *1G allele has been reported in high frequencies [90]. Because
CYP3A4 is responsible for metabolizing up to 50% of medications currently on the market [129, 142-144], any changes in its metabolism rate will affect the clearance of several medications. Also, any changes in CYP3A4 metabolism rates can be exaggerated when inherited with nonfunctional CYP3A5, like that
77 seen in the CSKT population. It is important to identify the effect of the
CYP3A4*1G genetic variant to better predict pharmacokinetic and pharmacodynamic changes to lead to increased drug efficacy and decreased toxicity.
78 References
1. Mancinelli, L., M. Cronin, and W. Sadee, Pharmacogenomics: the promise of personalized medicine. AAPS PharmSci, 2000. 2(1): p. E4. 2. Lee, S.S., Race, distributive justice and the promise of pharmacogenomics: ethical considerations. Am J Pharmacogenomics, 2003. 3(6): p. 385-92. 3. Ingelman-Sundberg, M., et al., Influence of cytochrome P450 polymorphisms on drug therapies: pharmacogenetic, pharmacoepigenetic and clinical aspects. Pharmacol Ther, 2007. 116(3): p. 496-526. 4. Paving the Way for Personalized Medicine: FDA's Role in a New Era of Medical Product Development. 2013, US Food and Drug Administration. 5. Maliepaard, M., et al., Pharmacogenetics in the evaluation of new drugs: a multiregional regulatory perspective. Nat Rev Drug Discov. 12(2): p. 103-15. 6. Lesko, L.J. and I. Zineh, DNA, drugs and chariots: on a decade of pharmacogenomics at the US FDA. Pharmacogenomics. 11(4): p. 507-12. 7. Table of Pharmacogenomic Biomarkers in Drug Labeling. 6/16/2014 [cited 2014 June 15]; http://www.fda.gov/drugs/scienceresearch/researchareas/pharmacogenetics/ucm 083378.htm]. 8. Lynch, T. and A. Price, The effect of cytochrome P450 metabolism on drug response, interactions, and adverse effects. Am Fam Physician, 2007. 76(3): p. 391-6. 9. Caudle, K.E., et al., Incorporation of pharmacogenomics into routine clinical practice: the Clinical Pharmacogenetics Implementation Consortium (CPIC) guideline development process. Curr Drug Metab, 2014. 15(2): p. 209-17. 10. Whirl-Carrillo, M., et al., Pharmacogenomics knowledge for personalized medicine. Clin Pharmacol Ther, 2012. 92(4): p. 414-7. 11. Borobia, A.M., et al., Trough tacrolimus concentrations in the first week after kidney transplantation are related to acute rejection. Ther Drug Monit, 2009. 31(4): p. 436-42. 12. Laskow, D.A., et al., An open-label, concentration-ranging trial of FK506 in primary kidney transplantation: a report of the United States Multicenter FK506 Kidney Transplant Group. Transplantation, 1996. 62(7): p. 900-5. 13. O'Seaghdha, C.M., et al., Higher tacrolimus trough levels on days 2-5 post-renal transplant are associated with reduced rates of acute rejection. Clin Transplant, 2009. 23(4): p. 462-8. 14. MacPhee, I.A., et al., The influence of pharmacogenetics on the time to achieve target tacrolimus concentrations after kidney transplantation. Am J Transplant, 2004. 4(6): p. 914-9. 15. Dai, Y., et al., Effect of CYP3A5 polymorphism on tacrolimus metabolic clearance in vitro. Drug Metab Dispos, 2006. 34(5): p. 836-47. 16. Zheng, S., et al., Measurement and compartmental modeling of the effect of CYP3A5 gene variation on systemic and intrarenal tacrolimus disposition. Clin Pharmacol Ther, 2012. 92(6): p. 737-45. 17. Quteineh, L., et al., Influence of CYP3A5 genetic polymorphism on tacrolimus daily dose requirements and acute rejection in renal graft recipients. Basic Clin Pharmacol Toxicol, 2008. 103(6): p. 546-52. 18. Macphee, I.A., et al., Tacrolimus pharmacogenetics: the CYP3A5*1 allele predicts low dose-normalized tacrolimus blood concentrations in whites and South Asians. Transplantation, 2005. 79(4): p. 499-502.
79 19. Thervet, E., et al., Optimization of initial tacrolimus dose using pharmacogenetic testing. Clin Pharmacol Ther, 2010. 87(6): p. 721-6. 20. Spear, B.B., M. Heath-Chiozzi, and J. Huff, Clinical application of pharmacogenetics. Trends Mol Med, 2001. 7(5): p. 201-4. 21. Preventing Medication Errors. 2006. 22. Rabinovitz, H., et al., [Adverse drug reactions definitions and terminology]. Harefuah, 2001. 140(12): p. 1181-6, 1228. 23. Edwards, I.R. and J.K. Aronson, Adverse drug reactions: definitions, diagnosis, and management. Lancet, 2000. 356(9237): p. 1255-9. 24. Partin, B., Preventing medication errors: an IOM Report. Nurse Pract, 2006. 31(12): p. 8. 25. Committee on Quality of Health Care in America Institute of Medicine. To Err Is Human: Building a Safer Health System, ed. L.T. Kohn, J.M. Corrigan, and M.S. Donaldson. 2000, Washington, D.C.: National Academy Press. 26. Lazarou, J., B.H. Pomeranz, and P.N. Corey, Incidence of adverse drug reactions in hospitalized patients: a meta-analysis of prospective studies. JAMA, 1998. 279(15): p. 1200-5. 27. de Wildt, S.N., et al., Cytochrome P450 3A: ontogeny and drug disposition. Clin Pharmacokinet, 1999. 37(6): p. 485-505. 28. Evans, W.E. and M.V. Relling, Pharmacogenomics: translating functional genomics into rational therapeutics. Science, 1999. 286(5439): p. 487-91. 29. Ingelman-Sundberg, M., Pharmacogenetics of cytochrome P450 and its applications in drug therapy: the past, present and future. Trends Pharmacol Sci, 2004. 25(4): p. 193-200. 30. Lamba, J.K., et al., Genetic contribution to variable human CYP3A-mediated metabolism. Adv Drug Deliv Rev, 2002. 54(10): p. 1271-94. 31. Kalow, W., B.K. Tang, and L. Endrenyi, Hypothesis: comparisons of inter- and intra-individual variations can substitute for twin studies in drug research. Pharmacogenetics, 1998. 8(4): p. 283-9. 32. Lamba, J.K., et al., Common allelic variants of cytochrome P4503A4 and their prevalence in different populations. Pharmacogenetics, 2002. 12(2): p. 121-32. 33. Tanaka, E., Update: genetic polymorphism of drug metabolizing enzymes in humans. J Clin Pharm Ther, 1999. 24(5): p. 323-9. 34. Bozina, N., V. Bradamante, and M. Lovric, Genetic polymorphism of metabolic enzymes P450 (CYP) as a susceptibility factor for drug response, toxicity, and cancer risk. Arh Hig Rada Toksikol, 2009. 60(2): p. 217-42. 35. Ma, Q. and A.Y. Lu, Pharmacogenetics, pharmacogenomics, and individualized medicine. Pharmacol Rev. 63(2): p. 437-59. 36. Sikka, R., et al., Bench to bedside: Pharmacogenomics, adverse drug interactions, and the cytochrome P450 system. Acad Emerg Med, 2005. 12(12): p. 1227-35. 37. Wijnen, P.A., et al., Review article: The prevalence and clinical relevance of cytochrome P450 polymorphisms. Aliment Pharmacol Ther, 2007. 26 Suppl 2: p. 211-9. 38. Caldwell, J., I. Gardner, and N. Swales, An introduction to drug disposition: the basic principles of absorption, distribution, metabolism, and excretion. Toxicol Pathol, 1995. 23(2): p. 102-14. 39. Benet, L.Z. and L.B. Sheiner, Pharmacokinetics: The dynamics of drug absorption distribution and elimination, in The Pharmacological Basis of Therapeutics, A. Gilman, et al., Editors. 1985, Macmillan: New York. p. 3-34.
80 40. Adoga, G.I., Introduction to drug metabolism: By G G Gibson and P Skett. pp 293. Chapman & Hall, London and New York. 1986.£12.95 ISBN 0-412-26400-5. Biochemical Education, 1987. 15(1): p. 50-50. 41. Wilkinson, G.R., Clearance approaches in pharmacology. Pharmacol Rev, 1987. 39(1): p. 1-47. 42. Rollins, D.E. and C.D. Klaassen, Biliary excretion of drugs in man. Clin Pharmacokinet, 1979. 4(5): p. 368-79. 43. Meijer, D., Transport and metabolism in the hepatobiliary system, in Handbook of Physiology of the Gastrointestinal System, S. Schulz, J. Forte, and B. Rauner, Editors. 1989, Oxford University Press: New York. 44. Rushmore, T.H. and A.N. Kong, Pharmacogenomics, regulation and signaling pathways of phase I and II drug metabolizing enzymes. Curr Drug Metab, 2002. 3(5): p. 481-90. 45. Hines, R.N. and D.G. McCarver, The ontogeny of human drug-metabolizing enzymes: phase I oxidative enzymes. J Pharmacol Exp Ther, 2002. 300(2): p. 355-60. 46. Danielson, P.B., The cytochrome P450 superfamily: biochemistry, evolution and drug metabolism in humans. Curr Drug Metab, 2002. 3(6): p. 561-97. 47. Girardin, F., Membrane transporter proteins: a challenge for CNS drug development. Dialogues Clin Neurosci, 2006. 8(3): p. 311-21. 48. Feng, M.R., Assessment of blood-brain barrier penetration: in silico, in vitro and in vivo. Curr Drug Metab, 2002. 3(6): p. 647-57. 49. Zanger, U.M., et al., Functional pharmacogenetics/genomics of human cytochromes P450 involved in drug biotransformation. Anal Bioanal Chem, 2008. 392(6): p. 1093-108. 50. Jaja, C., et al., Cytochrome p450 enzyme polymorphism frequency in indigenous and native american populations: a systematic review. Community Genet, 2008. 11(3): p. 141-9. 51. Nelson, D.R., et al., Comparison of cytochrome P450 (CYP) genes from the mouse and human genomes, including nomenclature recommendations for genes, pseudogenes and alternative-splice variants. Pharmacogenetics, 2004. 14(1): p. 1-18. 52. Nelson, D.R., Introductory remarks on human CYPs. Drug Metab Rev, 2002. 34(1-2): p. 1-5. 53. Denisov, I.G., et al., Structure and chemistry of cytochrome P450. Chem Rev, 2005. 105(6): p. 2253-77. 54. Oinonen, T. and K.O. Lindros, Zonation of hepatic cytochrome P-450 expression and regulation. Biochem J, 1998. 329 ( Pt 1): p. 17-35. 55. Ortiz de Montellano, P.R., The 1994 Bernard B. Brodie Award Lecture. Structure, mechanism, and inhibition of cytochrome P450. Drug Metab Dispos, 1995. 23(11): p. 1181-7. 56. Estabrook, R.W., The remarkable P450s: a historical overview of these versatile hemeprotein catalysts. FASEB J, 1996. 10(2): p. 202-4. 57. Nelson, D.R., et al., P450 superfamily: update on new sequences, gene mapping, accession numbers and nomenclature. Pharmacogenetics, 1996. 6(1): p. 1-42. 58. Vella, F., Four billion years: An essay on the evolution of genes and organisms: By W F Loomis. pp 286. Sinauer Associates, MA. 1988 ISBN 0-87893-476-6. Biochemical Education, 1989. 17(2): p. 107-107. 59. Nebert, D.W. and R. Feyereisen, Cytochrome P450, Biochemistry, Biophysics and Molecular Biology, ed. L. M.C. 1994, London: Libbey Eurotext.
81 60. Nebert, D.W. and F.J. Gonzalez, P450 genes: structure, evolution, and regulation. Annu Rev Biochem, 1987. 56: p. 945-93. 61. Nelson, D.R. and H.W. Strobel, Evolution of cytochrome P-450 proteins. Mol Biol Evol, 1987. 4(6): p. 572-93. 62. Gonzalez, F.J. and D.W. Nebert, Evolution of the P450 gene superfamily: animal- plant 'warfare', molecular drive and human genetic differences in drug oxidation. Trends Genet, 1990. 6(6): p. 182-6. 63. Monier, S., et al., Signals for the incorporation and orientation of cytochrome P450 in the endoplasmic reticulum membrane. J Cell Biol, 1988. 107(2): p. 457- 70. 64. Szczesna-Skorupa, E., et al., Positive charges at the NH2 terminus convert the membrane-anchor signal peptide of cytochrome P-450 to a secretory signal peptide. Proc Natl Acad Sci U S A, 1988. 85(3): p. 738-42. 65. Sakaguchi, M., K. Mihara, and R. Sato, A short amino-terminal segment of microsomal cytochrome P-450 functions both as an insertion signal and as a stop-transfer sequence. EMBO J, 1987. 6(8): p. 2425-31. 66. Hasemann, C.A., et al., Structure and function of cytochromes P450: a comparative analysis of three crystal structures. Structure, 1995. 3(1): p. 41-62. 67. Presnell, S.R. and F.E. Cohen, Topological distribution of four-alpha-helix bundles. Proc Natl Acad Sci U S A, 1989. 86(17): p. 6592-6. 68. Dawson, J.H., et al., Letter: Oxidized cytochrome P-450. Magnetic circular dichroism evidence for thiolate ligation in the substrate-bound form. Implications for the catalytic mechanism. J Am Chem Soc, 1976. 98(12): p. 3707-8. 69. Nebert, D.W., et al., Oxidases and Related Redox Systems, ed. T.H. King Mason and M. Morrison. 1988, New York: Liss. 70. Kimata, Y., et al., Role of Thr-252 in cytochrome P450cam: a study with unnatural amino acid mutagenesis. Biochem Biophys Res Commun, 1995. 208(1): p. 96-102. 71. Poulos, T.L., B.C. Finzel, and A.J. Howard, High-resolution crystal structure of cytochrome P450cam. J Mol Biol, 1987. 195(3): p. 687-700. 72. Imai, M., et al., Uncoupling of the cytochrome P-450cam monooxygenase reaction by a single mutation, threonine-252 to alanine or valine: possible role of the hydroxy amino acid in oxygen activation. Proc Natl Acad Sci U S A, 1989. 86(20): p. 7823-7. 73. Gotoh, O., Substrate recognition sites in cytochrome P450 family 2 (CYP2) proteins inferred from comparative analyses of amino acid and coding nucleotide sequences. J Biol Chem, 1992. 267(1): p. 83-90. 74. Pylypenko, O. and I. Schlichting, Structural aspects of ligand binding to and electron transfer in bacterial and fungal P450s. Annu Rev Biochem, 2004. 73: p. 991-1018. 75. Cytochrome P450: Structure, Mechanism, Biochemistry. 3rd ed, ed. O.d. Montellano. 2005, New York: Kluwer Academic/Plenum Publishers. 76. Belitsky, G.A. and M.G. Yakubovskaya, Genetic polymorphism and variability of chemical carcinogenesis. Biochemistry (Mosc), 2008. 73(5): p. 543-54. 77. Bernhardt, R., Cytochrome P450: structure, function, and generation of reactive oxygen species. Rev Physiol Biochem Pharmacol, 1996. 127: p. 137-221. 78. Akrawi, M., et al., Effects of phenobarbital and valproate on the expression of cytochromes P-450 in co-cultured rat hepatocytes. Toxicol In Vitro, 1993. 7(4): p. 477-80. 79. Rodgers, R.J., M.R. Waterman, and E.R. Simpson, Levels of messenger ribonucleic acid encoding cholesterol side-chain cleavage cytochrome P-450, 17
82 alpha-hydroxylase cytochrome P-450, adrenodoxin, and low density lipoprotein receptor in bovine follicles and corpora lutea throughout the ovarian cycle. Mol Endocrinol, 1987. 1(3): p. 274-9. 80. Giachelli, C.M. and C.J. Omiecinski, Developmental regulation of cytochrome P- 450 genes in the rat. Mol Pharmacol, 1987. 31(5): p. 477-84. 81. Waxman, D.J. and L. Azaroff, Phenobarbital induction of cytochrome P-450 gene expression. Biochem J, 1992. 281 ( Pt 3): p. 577-92. 82. Moore, L.B., et al., Orphan nuclear receptors constitutive androstane receptor and pregnane X receptor share xenobiotic and steroid ligands. J Biol Chem, 2000. 275(20): p. 15122-7. 83. Pascussi, J.M., et al., Interleukin-6 negatively regulates the expression of pregnane X receptor and constitutively activated receptor in primary human hepatocytes. Biochem Biophys Res Commun, 2000. 274(3): p. 707-13. 84. Gerbal-Chaloin, S., et al., Transcriptional regulation of CYP2C9 gene. Role of glucocorticoid receptor and constitutive androstane receptor. J Biol Chem, 2002. 277(1): p. 209-17. 85. Lehmann, J.M., et al., The human orphan nuclear receptor PXR is activated by compounds that regulate CYP3A4 gene expression and cause drug interactions. J Clin Invest, 1998. 102(5): p. 1016-23. 86. Whitlock, J.P., Jr., Induction of cytochrome P4501A1. Annu Rev Pharmacol Toxicol, 1999. 39: p. 103-25. 87. Niemira, M., A. Wisniewska, and Z. Mazerska, [Polymorphism and the level of P450 gene expression in xenobiotic metabolism]. Postepy Biochem, 2009. 55(3): p. 279-89. 88. Mode, A. and G. Norstedt, Effects of gonadal steroid hormones on the hypothalamo-pituitary-liver axis in the control of sex differences in hepatic steroid metabolism in the rat. J Endocrinol, 1982. 95(2): p. 181-7. 89. Best, L.G., et al., IBC CARe microarray allelic population prevalences in an American Indian population. PLoS One. 8(9): p. e75080. 90. Fohner, A., et al., Pharmacogenetics in American Indian populations: analysis of CYP2D6, CYP3A4, CYP3A5, and CYP2C9 in the Confederated Salish and Kootenai Tribes. Pharmacogenet Genomics, 2013. 23(8): p. 403-14. 91. Schwarz, U.I., Clinical relevance of genetic polymorphisms in the human CYP2C9 gene. Eur J Clin Invest, 2003. 33 Suppl 2: p. 23-30. 92. Kirchheiner, J. and J. Brockmoller, Clinical consequences of cytochrome P450 2C9 polymorphisms. Clin Pharmacol Ther, 2005. 77(1): p. 1-16. 93. Lee, C.R., J.A. Goldstein, and J.A. Pieper, Cytochrome P450 2C9 polymorphisms: a comprehensive review of the in-vitro and human data. Pharmacogenetics, 2002. 12(3): p. 251-63. 94. Ingelman-Sundberg, M., Genetic polymorphisms of cytochrome P450 2D6 (CYP2D6): clinical consequences, evolutionary aspects and functional diversity. Pharmacogenomics J, 2005. 5(1): p. 6-13. 95. Eichelbaum, M., M. Ingelman-Sundberg, and W.E. Evans, Pharmacogenomics and individualized drug therapy. Annu Rev Med, 2006. 57: p. 119-37. 96. Dalen, P., et al., Disposition of debrisoquine in Caucasians with different CYP2D6-genotypes including those with multiple genes. Pharmacogenetics, 1999. 9(6): p. 697-706. 97. Shimada, T., et al., Interindividual variations in human liver cytochrome P-450 enzymes involved in the oxidation of drugs, carcinogens and toxic chemicals: studies with liver microsomes of 30 Japanese and 30 Caucasians. J Pharmacol Exp Ther, 1994. 270(1): p. 414-23.
83 98. Kolars, J.C., et al., CYP3A gene expression in human gut epithelium. Pharmacogenetics, 1994. 4(5): p. 247-59. 99. Yamazaki, H. and T. Shimada, Progesterone and testosterone hydroxylation by cytochromes P450 2C19, 2C9, and 3A4 in human liver microsomes. Arch Biochem Biophys, 1997. 346(1): p. 161-9. 100. Kuehl, P., et al., Sequence diversity in CYP3A promoters and characterization of the genetic basis of polymorphic CYP3A5 expression. Nat Genet, 2001. 27(4): p. 383-91. 101. Garcia-Martin, E., et al., CYP3A4 variant alleles in white individuals with low CYP3A4 enzyme activity. Clin Pharmacol Ther, 2002. 71(3): p. 196-204. 102. Sata, F., et al., CYP3A4 allelic variants with amino acid substitutions in exons 7 and 12: evidence for an allelic variant with altered catalytic activity. Clin Pharmacol Ther, 2000. 67(1): p. 48-56. 103. Eiselt, R., et al., Identification and functional characterization of eight CYP3A4 protein variants. Pharmacogenetics, 2001. 11(5): p. 447-58. 104. Dai, D., et al., Identification of variants of CYP3A4 and characterization of their abilities to metabolize testosterone and chlorpyrifos. J Pharmacol Exp Ther, 2001. 299(3): p. 825-31. 105. van Schaik, R.H., et al., The CYP3A4*3 allele: is it really rare? Clin Chem, 2001. 47(6): p. 1104-6. 106. Rodriguez-Antona, C., et al., Phenotype-genotype variability in the human CYP3A locus as assessed by the probe drug quinine and analyses of variant CYP3A4 alleles. Biochem Biophys Res Commun, 2005. 338(1): p. 299-305. 107. Rebbeck, T.R., et al., Modification of clinical presentation of prostate tumors by a novel genetic variant in CYP3A4. J Natl Cancer Inst, 1998. 90(16): p. 1225-9. 108. Felix, C.A., et al., Association of CYP3A4 genotype with treatment-related leukemia. Proc Natl Acad Sci U S A, 1998. 95(22): p. 13176-81. 109. Amirimani, B., et al., RESPONSE: re: modification of clinical presentation of prostate tumors by a novel genetic variant in CYP3A4. J Natl Cancer Inst, 1999. 91(18): p. 1588-90. 110. Ando, Y., et al., Re: Modification of clinical presentation of prostate tumors by a novel genetic variant in CYP3A4. J Natl Cancer Inst, 1999. 91(18): p. 1587-90. 111. Spurdle, A.B., et al., The CYP3A4*1B polymorphism has no functional significance and is not associated with risk of breast or ovarian cancer. Pharmacogenetics, 2002. 12(5): p. 355-66. 112. Westlind, A., et al., Interindividual differences in hepatic expression of CYP3A4: relationship to genetic polymorphism in the 5'-upstream regulatory region. Biochem Biophys Res Commun, 1999. 259(1): p. 201-5. 113. Paris, P.L., et al., Association between a CYP3A4 genetic variant and clinical presentation in African-American prostate cancer patients. Cancer Epidemiol Biomarkers Prev, 1999. 8(10): p. 901-5. 114. Wojnowski, L., et al., Re: modification of clinical presentation of prostate tumors by a novel genetic variant in CYP3A4. J Natl Cancer Inst, 2002. 94(8): p. 630-1; author reply 631-2. 115. Liu, C.H., et al., Screening CYP3A single nucleotide polymorphisms in a Han Chinese population with a genotyping chip. Pharmacogenomics, 2005. 6(7): p. 731-47. 116. Zhang, W., et al., Influence of CYP3A5*3 polymorphism and interaction between CYP3A5*3 and CYP3A4*1G polymorphisms on post-operative fentanyl analgesia in Chinese patients undergoing gynaecological surgery. Eur J Anaesthesiol, 2011. 28(4): p. 245-50.
84 117. Dong, Z.L., et al., Effect of CYP3A4*1G on the fentanyl consumption for intravenous patient-controlled analgesia after total abdominal hysterectomy in Chinese Han population. J Clin Pharm Ther, 2011. 37(2): p. 153-6. 118. Gao, Y., L.R. Zhang, and Q. Fu, CYP3A4*1G polymorphism is associated with lipid-lowering efficacy of atorvastatin but not of simvastatin. Eur J Clin Pharmacol, 2008. 64(9): p. 877-82. 119. Zhang, W., et al., CYP3A4*1G genetic polymorphism influences CYP3A activity and response to fentanyl in Chinese gynecologic patients. Eur J Clin Pharmacol, 2010. 66(1): p. 61-6. 120. Yuan, R., et al., Impact of CYP3A4*1G polymorphism on metabolism of fentanyl in Chinese patients undergoing lower abdominal surgery. Clin Chim Acta, 2011. 412(9-10): p. 755-60. 121. The International HapMap Project. Nature, 2003. 426(6968): p. 789-96. 122. Wang, D., et al., Intronic polymorphism in CYP3A4 affects hepatic expression and response to statin drugs. Pharmacogenomics J, 2011. 11(4): p. 274-86. 123. Elens, L., et al., A new functional CYP3A4 intron 6 polymorphism significantly affects tacrolimus pharmacokinetics in kidney transplant recipients. Clin Chem, 2011. 57(11): p. 1574-83. 124. Okubo, M., et al., CYP3A4 intron 6 C>T polymorphism (CYP3A4*22) is associated with reduced CYP3A4 protein level and function in human liver microsomes. J Toxicol Sci, 2013. 38(3): p. 349-54. 125. Elens, L., et al., Novel CYP3A4 intron 6 single nucleotide polymorphism is associated with simvastatin-mediated cholesterol reduction in the Rotterdam Study. Pharmacogenet Genomics, 2011. 21(12): p. 861-6. 126. Elens, L., et al., Effect of a new functional CYP3A4 polymorphism on calcineurin inhibitors' dose requirements and trough blood levels in stable renal transplant patients. Pharmacogenomics, 2011. 12(10): p. 1383-96. 127. Haehner, B.D., et al., Bimodal distribution of renal cytochrome P450 3A activity in humans. Mol Pharmacol, 1996. 50(1): p. 52-9. 128. Huang, Z., et al., Expression of cytochromes P450 in human breast tissue and tumors. Drug Metab Dispos, 1996. 24(8): p. 899-905. 129. Xie, H.G., et al., Genetic variability in CYP3A5 and its possible consequences. Pharmacogenomics, 2004. 5(3): p. 243-72. 130. Kivisto, K.T., et al., Expression of cytochrome P 450 3A enzymes in human lung: a combined RT-PCR and immunohistochemical analysis of normal tissue and lung tumours. Naunyn Schmiedebergs Arch Pharmacol, 1996. 353(2): p. 207-12. 131. Thummel, K.E. and G.R. Wilkinson, In vitro and in vivo drug interactions involving human CYP3A. Annu Rev Pharmacol Toxicol, 1998. 38: p. 389-430. 132. Williams, J.A., et al., Comparative metabolic capabilities of CYP3A4, CYP3A5, and CYP3A7. Drug Metab Dispos, 2002. 30(8): p. 883-91. 133. Hustert, E., et al., The genetic determinants of the CYP3A5 polymorphism. Pharmacogenetics, 2001. 11(9): p. 773-9. 134. Chou, F.C., S.J. Tzeng, and J.D. Huang, Genetic polymorphism of cytochrome P450 3A5 in Chinese. Drug Metab Dispos, 2001. 29(9): p. 1205-9. 135. PharmGKB. [cited 2014 June 15]; www.pharmgkb.org]. 136. Wolbold, R., et al., Sex is a major determinant of CYP3A4 expression in human liver. Hepatology, 2003. 38(4): p. 978-88. 137. Miura, M., et al., Impact of the CYP3A4*1G polymorphism and its combination with CYP3A5 genotypes on tacrolimus pharmacokinetics in renal transplant patients. Pharmacogenomics, 2011. 12(7): p. 977-84.
85 138. Zuo, X.C., et al., Effects of CYP3A4 and CYP3A5 polymorphisms on tacrolimus pharmacokinetics in Chinese adult renal transplant recipients: a population pharmacokinetic analysis. Pharmacogenet Genomics, 2012. 23(5): p. 251-61. 139. He, B.X., et al., A functional polymorphism in the CYP3A4 gene is associated with increased risk of coronary heart disease in the Chinese Han population. Basic Clin Pharmacol Toxicol, 2010. 108(3): p. 208-13. 140. Du, J., et al., Relationship between response to risperidone, plasma concentrations of risperidone and CYP3A4 polymorphisms in schizophrenia patients. J Psychopharmacol, 2010. 24(7): p. 1115-20. 141. Finta, C. and P.G. Zaphiropoulos, The human cytochrome P450 3A locus. Gene evolution by capture of downstream exons. Gene, 2000. 260(1-2): p. 13-23. 142. Sadee, W., Gene-gene-environment interactions between drugs, transporters, receptors, and metabolizing enzymes: Statins, SLCO1B1, and CYP3A4 as an example. J Pharm Sci, 2013. 102(9): p. 2924-9. 143. Kapelyukh, Y., et al., Multiple substrate binding by cytochrome P450 3A4: estimation of the number of bound substrate molecules. Drug Metab Dispos, 2008. 36(10): p. 2136-44. 144. Guengerich, F.P., et al., Twenty years of biochemistry of human P450s: purification, expression, mechanism, and relevance to drugs. Drug Metab Dispos, 1998. 26(12): p. 1175-8. 145. Sim, S.C., et al., CYP3A7 protein expression is high in a fraction of adult human livers and partially associated with the CYP3A7*1C allele. Pharmacogenet Genomics, 2005. 15(9): p. 625-31. 146. Burk, O., et al., Molecular mechanisms of polymorphic CYP3A7 expression in adult human liver and intestine. J Biol Chem, 2002. 277(27): p. 24280-8. 147. Tateishi, T., et al., No ethnic difference between Caucasian and Japanese hepatic samples in the expression frequency of CYP3A5 and CYP3A7 proteins. Biochem Pharmacol, 1999. 57(8): p. 935-9. 148. Gu, J., et al., Expression of biotransformation enzymes in human fetal olfactory mucosa: potential roles in developmental toxicity. Toxicol Appl Pharmacol, 2000. 165(2): p. 158-62. 149. Hashimoto, H., et al., Gene structure of CYP3A4, an adult-specific form of cytochrome P450 in human livers, and its transcriptional control. Eur J Biochem, 1993. 218(2): p. 585-95. 150. Inoue, K., et al., Assignment of the human cytochrome P-450 nifedipine oxidase gene (CYP3A4) to chromosome 7 at band q22.1 by fluorescence in situ hybridization. Jpn J Hum Genet, 1992. 37(2): p. 133-8. 151. Scott, E.E. and J.R. Halpert, Structures of cytochrome P450 3A4. Trends Biochem Sci, 2005. 30(1): p. 5-7. 152. Guengerich, F.P., Cytochrome P-450 3A4: regulation and role in drug metabolism. Annu Rev Pharmacol Toxicol, 1999. 39: p. 1-17. 153. Korzekwa, K.R., et al., Evaluation of atypical cytochrome P450 kinetics with two- substrate models: evidence that multiple substrates can simultaneously bind to cytochrome P450 active sites. Biochemistry, 1998. 37(12): p. 4137-47. 154. Ozdemir, V., et al., Evaluation of the genetic component of variability in CYP3A4 activity: a repeated drug administration method. Pharmacogenetics, 2000. 10(5): p. 373-88. 155. Watkins, P.B., Cyclosporine and liver transplantation: will the midazolam test make blood level monitoring obsolete? Hepatology, 1995. 22(3): p. 994-6.
86 156. Lin, Y.S., et al., Co-regulation of CYP3A4 and CYP3A5 and contribution to hepatic and intestinal midazolam metabolism. Mol Pharmacol, 2002. 62(1): p. 162-72. 157. Floyd, M.D., et al., Genotype-phenotype associations for common CYP3A4 and CYP3A5 variants in the basal and induced metabolism of midazolam in European- and African-American men and women. Pharmacogenetics, 2003. 13(10): p. 595-606. 158. Lin, Y.S., et al., In-vivo phenotyping for CYP3A by a single-point determination of midazolam plasma concentration. Pharmacogenetics, 2001. 11(9): p. 781-91. 159. Markowitz, J.S., et al., Effect of St John's wort on drug metabolism by induction of cytochrome P450 3A4 enzyme. JAMA, 2003. 290(11): p. 1500-4. 160. Ohno, Y., et al., General framework for the prediction of oral drug interactions caused by CYP3A4 induction from in vivo information. Clin Pharmacokinet, 2008. 47(10): p. 669-80. 161. Schellens, J.H., P.A. Soons, and D.D. Breimer, Lack of bimodality in nifedipine plasma kinetics in a large population of healthy subjects. Biochem Pharmacol, 1988. 37(13): p. 2507-10. 162. Aoyama, T., et al., Cytochrome P-450 hPCN3, a novel cytochrome P-450 IIIA gene product that is differentially expressed in adult human liver. cDNA and deduced amino acid sequence and distinct specificities of cDNA-expressed hPCN1 and hPCN3 for the metabolism of steroid hormones and cyclosporine. J Biol Chem, 1989. 264(18): p. 10388-95. 163. Burk, O., et al., The induction of cytochrome P450 3A5 (CYP3A5) in the human liver and intestine is mediated by the xenobiotic sensors pregnane X receptor (PXR) and constitutively activated receptor (CAR). J Biol Chem, 2004. 279(37): p. 38379-85. 164. Wrighton, S.A., et al., Studies on the expression and metabolic capabilities of human liver cytochrome P450IIIA5 (HLp3). Mol Pharmacol, 1990. 38(2): p. 207- 13. 165. Wilke, R.A., J.H. Moore, and J.K. Burmester, Relative impact of CYP3A genotype and concomitant medication on the severity of atorvastatin-induced muscle damage. Pharmacogenet Genomics, 2005. 15(6): p. 415-21. 166. Wandel, C., et al., CYP3A activity in African American and European American men: population differences and functional effect of the CYP3A4*1B5'-promoter region polymorphism. Clin Pharmacol Ther, 2000. 68(1): p. 82-91. 167. McDonagh, E.M., et al., From pharmacogenomic knowledge acquisition to clinical applications: the PharmGKB as a clinical pharmacogenomic biomarker resource. Biomark Med, 2011. 5(6): p. 795-806. 168. Home Page of the Human Cytochrome P450 (CYP) Allele Nomenclature Committee. [cited 2014 June 1]; http://www.cypalleles.ki.se.htm]. 169. Abecasis, G.R., et al., An integrated map of genetic variation from 1,092 human genomes. Nature, 2012. 491(7422): p. 56-65. 170. Coon, M.J., Enzyme ingenuity in biological oxidations: a trail leading to cytochrome p450. J Biol Chem, 2002. 277(32): p. 28351-63. 171. Marathe, P.H., W.C. Shyu, and W.G. Humphreys, The use of radiolabeled compounds for ADME studies in discovery and exploratory development. Curr Pharm Des, 2004. 10(24): p. 2991-3008. 172. Streetman, D.S., et al., Combined phenotypic assessment of CYP1A2, CYP2C19, CYP2D6, CYP3A, N-acetyltransferase-2, and xanthine oxidase with the "Cooperstown cocktail". Clin Pharmacol Ther, 2000. 68(4): p. 375-83.
87 173. Gashaw, I., et al., Cytochrome p450 3A4 messenger ribonucleic acid induction by rifampin in human peripheral blood mononuclear cells: correlation with alprazolam pharmacokinetics. Clin Pharmacol Ther, 2003. 74(5): p. 448-57. 174. Andersen, M.R., F.M. Farin, and C.J. Omiecinski, Quantification of multiple human cytochrome P450 mRNA molecules using competitive reverse transcriptase-PCR. DNA Cell Biol, 1998. 17(3): p. 231-8. 175. Haas, C.E., et al., Cytochrome P450 mRNA expression in peripheral blood lymphocytes as a predictor of enzyme induction. Eur J Clin Pharmacol, 2005. 61(8): p. 583-93. 176. Rodak, B., G. Fritsma, and K. Doig, Hematology: Clinical Principles and Applications. 3rd ed. 2007, St. Louis, Missouri: Saunders Elsevier. 177. Raucy, J.L., et al., Drug metabolizing enzymes in lymphocytes. J Biochem Mol Toxicol, 1999. 13(3-4): p. 223-6. 178. Siest, G., et al., Transcription factor and drug-metabolizing enzyme gene expression in lymphocytes from healthy human subjects. Drug Metab Dispos, 2008. 36(1): p. 182-9. 179. Finnstrom, N., et al., Independent patterns of cytochrome P450 gene expression in liver and blood in patients with suspected liver disease. Eur J Clin Pharmacol, 2001. 57(5): p. 403-9. 180. Nakamoto, T., et al., Quantitative RT-PCR for CYP3A4 mRNA in human peripheral lymphocytes: induction of CYP3A4 in lymphocytes and in liver by rifampicin. Pharmacogenetics, 2000. 10(6): p. 571-5. 181. Koch, I., et al., Interindividual variability and tissue-specificity in the expression of cytochrome P450 3A mRNA. Drug Metab Dispos, 2002. 30(10): p. 1108-14. 182. Nowakowski-Gashaw, I., et al., Rapid quantification of CYP3A4 expression in human leukocytes by real-time reverse transcription-PCR. Clin Chem, 2002. 48(2): p. 366-70. 183. Janardan, S.K., et al., Selective expression of CYP3A5 and not CYP3A4 in human blood. Pharmacogenetics, 1996. 6(5): p. 379-85. 184. Sempoux, C., et al., Cytochrome P450 3A proteins are expressed in B lymphocytes but not in T lymphocytes. Pharmacogenetics, 1999. 9(2): p. 263-5. 185. Starkel, P., et al., CYP 3A proteins are expressed in human neutrophils and lymphocytes but are not induced by rifampicin. Life Sci, 1999. 64(8): p. 643-53. 186. Krovat, B.C., J.H. Tracy, and C.J. Omiecinski, Fingerprinting of cytochrome P450 and microsomal epoxide hydrolase gene expression in human blood cells. Toxicol Sci, 2000. 55(2): p. 352-60. 187. Iwatsubo, T., et al., Prediction of in vivo drug metabolism in the human liver from in vitro metabolism data. Pharmacol Ther, 1997. 73(2): p. 147-71. 188. Houston, J.B. and D.J. Carlile, Prediction of hepatic clearance from microsomes, hepatocytes, and liver slices. Drug Metab Rev, 1997. 29(4): p. 891-922. 189. Ekins, S., et al., Present and future in vitro approaches for drug metabolism. J Pharmacol Toxicol Methods, 2000. 44(1): p. 313-24. 190. Sugita, O., et al., Kinetic analysis of tolbutamide-sulfonamide interaction in rabbits based on clearance concept. Prediction of species difference from in vitro plasma protein binding and metabolism. Drug Metab Dispos, 1984. 12(1): p. 131- 8. 191. Rane, A., G.R. Wilkinson, and D.G. Shand, Prediction of hepatic extraction ratio from in vitro measurement of intrinsic clearance. J Pharmacol Exp Ther, 1977. 200(2): p. 420-4.
88 192. Kroemer, H.K., et al., Predictability of the in vivo metabolism of verapamil from in vitro data: contribution of individual metabolic pathways and stereoselective aspects. J Pharmacol Exp Ther, 1992. 260(3): p. 1052-7. 193. Iwatsubo, T., et al., Prediction of in vivo drug disposition from in vitro data based on physiological pharmacokinetics. Biopharm Drug Dispos, 1996. 17(4): p. 273- 310. 194. Obach, R.S., et al., The prediction of human pharmacokinetic parameters from preclinical and in vitro metabolism data. J Pharmacol Exp Ther, 1997. 283(1): p. 46-58. 195. Obach, R.S., Prediction of human clearance of twenty-nine drugs from hepatic microsomal intrinsic clearance data: An examination of in vitro half-life approach and nonspecific binding to microsomes. Drug Metab Dispos, 1999. 27(11): p. 1350-9. 196. Rodrigues, A.D. and S.L. Wong, Application of human liver microsomes in metabolism-based drug-drug interactions: in vitro-in vivo correlations and the Abbott Laboratories experience. Adv Pharmacol, 1997. 43: p. 65-101. 197. Jia, L. and X. Liu, The conduct of drug metabolism studies considered good practice (II): in vitro experiments. Curr Drug Metab, 2007. 8(8): p. 822-9. 198. Bjornsson, T.D., et al., The conduct of in vitro and in vivo drug-drug interaction studies: a PhRMA perspective. J Clin Pharmacol, 2003. 43(5): p. 443-69. 199. Dostalek, M., et al., Significantly reduced cytochrome P450 3A4 expression and activity in liver from humans with diabetes mellitus. Br J Pharmacol, 2010. 163(5): p. 937-47. 200. Aitken, A.E. and E.T. Morgan, Gene-specific effects of inflammatory cytokines on cytochrome P450 2C, 2B6 and 3A4 mRNA levels in human hepatocytes. Drug Metab Dispos, 2007. 35(9): p. 1687-93. 201. Easterbrook, J., et al., Effects of organic solvents on the activities of cytochrome P450 isoforms, UDP-dependent glucuronyl transferase, and phenol sulfotransferase in human hepatocytes. Drug Metab Dispos, 2001. 29(2): p. 141- 4. 202. He, P., et al., Human pregnane X receptor: genetic polymorphisms, alternative mRNA splice variants, and cytochrome P450 3A metabolic activity. J Clin Pharmacol, 2006. 46(11): p. 1356-69. 203. Lamba, J., V. Lamba, and E. Schuetz, Genetic variants of PXR (NR1I2) and CAR (NR1I3) and their implications in drug metabolism and pharmacogenetics. Curr Drug Metab, 2005. 6(4): p. 369-83.
89